Twenty-Four-Hour Ambulatory Blood Pressure Monitoring Parameters During Pregnancy: A Pilot Study

Introduction: Maternal blood pressure (BP) is a critical cardiovascular marker with profound implications for maternal and fetal well-being, particularly in the detection of hypertensive disorders during pregnancy. Although conventional clinic-based BP (CBP) measurements have traditionvally been used, monitoring 24-hour ambulatory BP (ABP) has emerged as a more reliable method for assessing BP levels and diagnosing conditions such as gestational hypertension and preeclampsia/eclampsia. This study aimed to assess the feasibility and acceptability of 24-hour ABP monitoring in pregnant women and report on various ABP parameters, including ambulatory blood pressure variability (ABPV). Method: A prospective cross-sectional study design was employed, involving 55 multipara pregnant women with and without prior adverse pregnancy outcomes (APOs). The participants underwent baseline assessments, including anthropometrics, resting CBP measurements, and the placement of ABP and actigraphy devices. Following a 24-hour period with these devices, participants shared their experiences to gauge device acceptability. Pregnancy outcomes were collected postpartum. Results: Twenty-four-hour ABP monitoring before 20 weeks of gestation is feasible for women with and without prior APOs. Although some inconvenience was noted, the majority of participants wore the ABP monitoring device for the entire 24-hour period. Pregnant women who later experienced APOs exhibited higher 24-hour ABP and ABPV values in the early stages of pregnancy. Conclusion: The study highlights the potential benefits of 24-hour ABP monitoring as a valuable tool in prenatal care, emphasizing the need for further research in this area.

Effectiveness of Oral Iron Therapy in Anemic Inpatient Pregnant Women: A Single Center Retrospective Cohort Study

Background and aim Oral iron therapy is effective in treating iron deficiency anemia in outpatient pregnant women but has not been studied in inpatient pregnant women. We aimed to evaluate the effect of oral iron therapy versus no therapy during hospitalization on maternal and neonatal outcomes in women with anemia who are hospitalized for pregnancy-related morbidities (i.e., preterm premature rupture of membranes, preterm labor, pre-eclampsia, abnormal placentation, or fetal monitoring). Methods A retrospective, single-center study was conducted in hospitalized pregnant women (2018 to 2020) with inpatient stays of more than three days. The primary outcome was a change in hemoglobin level from admission to delivery in women treated with oral iron compared with those left untreated. Secondary outcomes included the total amount of iron administered before delivery, the time interval from admission to delivery, and neonatal effects. Results Two hundred sixty-three women were admitted, 79 women had anemia, and 29 (36.7%) received at least one dose of oral iron. Baseline patient characteristics were similar between groups. The median (interquartile range) dose of iron in the oral iron group was 1185.0 (477.0, 1874.0) mg. Neither absolute hemoglobin before delivery (control group: 10.0±1.2 g/dL; iron group: 10.1±1.1 g/dL; p=0.774) nor change in hemoglobin from admission to delivery (control group: -0.1±1.1 g/dL vs. iron group: 0.4±1.1 g/dL; p=0.232) differed between groups. Women in the control group had shorter length of stay (LOS) median (IQR) than women in the iron group (control group: 7.1 (5.0, 13.7) days; iron group: 11.4 (7.4, 25.9) days; p=0.03). There were no differences in maternal mode of delivery, though each group had high rates of cesarean delivery (control group: 53.7%; iron group: 72.4%; p=0.181). There were no differences in estimated blood loss at delivery (control group: 559±401; iron group: 662.1±337.4;p=0.264) in either group. Neonatal birthweight (control group: 1.9±0.7 kg; iron group: 1.9±0.7 kg; p=0.901), birth hemoglobin (control group: 16.3±2.2 g/dL; iron group: 16±2.2 g/dL; p=0.569), neonatal intensive care unit (NICU) admission (control group: 93.3%; iron group: 84.8%;p=0.272 ), or neonatal death (control group: 8.9%; iron group: 3%; p=0.394) were not different between groups. Conclusions Oral iron administered to anemic inpatient pregnant women was not associated with higher hemoglobin concentrations before delivery. Lack of standardized iron regimens and short hospital stays may contribute to the inefficacy of oral iron for this inpatient pregnant population. The small sample size and retrospective nature of this study are limiting factors in drawing conclusive evidence from this study.

Age-Stratified Sex Differences in Heart Failure With Preserved Ejection Fraction Among Adult Hospitalizations

BACKGROUND

There is evidence that heart failure with preserved ejection fraction (HFpEF)-related hospitalizations are increasing in the United States. However, there is a lack of knowledge about HFpEF-related hospitalizations among younger adults.

OBJECTIVE

The aims of this study were to perform a retrospective analysis using the Nationwide Inpatient Sample and to examine age-stratified sex differences in the prevalence, correlates, and outcomes of HFpEF-related hospitalization across the adult life span.

METHOD

Using the Nationwide Inpatient Sample (2002-2014), patient and hospital characteristics were determined. Joinpoint regression was used to describe age-stratified sex differences in the annual average percent change of hospitalizations with HFpEF. Survey logistic regression was used to estimate adjusted odds ratios representing the association of sex with HFpEF-related hospitalization and in-hospital mortality.

RESULTS

There were 8 599 717 HFpEF-related hospitalizations (2.43% of all hospitalizations). Women represented the majority (5 459 422 [63.48%]) of HFpEF-related adult hospitalizations, compared with men (3 140 295 [36.52%]). Compared with men younger than 50 years, women within the same age group were 6% to 28% less likely to experience HFpEF-related hospitalization. Comorbidities such as hypertensive heart disease, renal disease, hypertension, obstructive sleep apnea, atrial fibrillation, obesity, anemia, and pulmonary edema explained a greater proportion of the risk of HFpEF-related hospitalization in adults younger than 50 years than in adults 50 years or older.

CONCLUSION

Before the age of 50 years, women exhibit lower HFpEF-related hospitalization than men, a pattern that reverses with advancing age. Understanding and addressing the factors contributing to these sex-specific differences can have several potential implications for improving women's cardiovascular health.

Treatment of Severe Symptomatic Aortic Stenosis During Pregnancy: A Potential Role for TAVR?

A 35-year-old woman presented at 22 weeks gestation with severe symptomatic aortic stenosis with a mean gradient of 94 mm Hg and an aortic valve area of 0.53 cm2. After multidisciplinary discussion, she underwent transcatheter aortic valve replacement during pregnancy.

Associations between area-level arsenic exposure and adverse birth outcomes: An Echo-wide cohort analysis

BACKGROUND

Drinking water is a common source of exposure to inorganic arsenic. In the US, the Safe Drinking Water Act (SDWA) was enacted to protect consumers from exposure to contaminants, including arsenic, in public water systems (PWS). The reproductive effects of preconception and prenatal arsenic exposure in regions with low to moderate arsenic concentrations are not well understood.

OBJECTIVES

This study examined associations between preconception and prenatal exposure to arsenic violations in water, measured via residence in a county with an arsenic violation in a regulated PWS during pregnancy, and five birth outcomes: birth weight, gestational age at birth, preterm birth, small for gestational age (SGA), and large for gestational age (LGA).

METHODS

Data for arsenic violations in PWS, defined as concentrations exceeding 10 parts per billion, were obtained from the Safe Drinking Water Information System. Participants of the Environmental influences on Child Health Outcomes Cohort Study were matched to arsenic violations by time and location based on residential history data. Multivariable, mixed effects regression models were used to assess the relationship between preconception and prenatal exposure to arsenic violations in drinking water and birth outcomes.

RESULTS

Compared to unexposed infants, continuous exposure to arsenic from three months prior to conception through birth was associated with 88.8 g higher mean birth weight (95% CI: 8.2, 169.5), after adjusting for individual-level confounders. No statistically significant associations were observed between any preconception or prenatal violations exposure and gestational age at birth, preterm birth, SGA, or LGA.

CONCLUSIONS

Our study did not identify associations between preconception and prenatal arsenic exposure, defined by drinking water exceedances, and adverse birth outcomes. Exposure to arsenic violations in drinking water was associated with higher birth weight. Future studies would benefit from more precise geodata of water system service areas, direct household drinking water measurements, and exposure biomarkers.

Pregnancy-Associated Takotsubo Cardiomyopathy Hospitalizations in the United States

Takotsubo cardiomyopathy (TCM) is most common in postmenopausal women aged ≥ 50 years but also affects pregnant individuals. However, there are no national estimates on the prevalence, timing of occurrence, correlates, and outcomes of Pregnancy-associated TCM. Using the Nationwide Inpatient Sample (NIS: 2016-2020), we describe rates of pregnancy-associated TCM hospitalizations among 13 - 49 years old pregnant individuals in the United States by selected demographic, behavioral, hospital, and clinical characteristics. Joinpoint regression was used to describe the Annual Average Percent Change of pregnancy-associated TCM hospitalizations. Survey logistic regression was used to measure the association of pregnancy-associated TCM hospitalizations with maternal outcomes. Of the 19,754,535 pregnancy-associated hospitalizations, 590 were TCM-associated. The overall trend in pregnancy-associated TCM hospitalizations remained stable during the study period. The majority of TCM occurred during the postpartum, followed by antepartum and delivery-associated hospitalizations. Compared to pregnancy hospitalizations without TCM, those with TCM were more likely to be over the age of 35 years and use tobacco and opioids. Comorbidities during TCM-associated pregnancy hospitalizations included heart failure, coronary artery disease, hemorrhagic stroke, and hypertension. After controlling for potential confounders, the odds of pregnancy-associated TCM hospitalizations were 98.7 times (aOR 98.66, 95% CI: 31.23, 311.64) and 14.7 times (aOR 14.75, 95% CI: 9.99, 21.76) higher for experiencing in-hospital mortality and a prolonged hospital stay, respectively, than those without TCM. Although rare, pregnancy-associated TCM hospitalization is more likely to occur during the postpartum period and is associated with in-hospital mortality and prolonged hospital stay.

Two Uncomplicated Pregnancies on Alectinib in a Woman With Metastatic ALK-Rearranged NSCLC: A Case Report

Lung cancer incidence is increasing in pregnancy due in part to advanced maternal age. A subset of patients with NSCLC during pregnancy harbor an ALK gene rearrangement. Although ALK inhibitors, such as alectinib, are routinely used to treat ALK-rearranged NSCLC, there are limited safety data regarding use during pregnancy and fetal effects. Here, we report the second case of a patient with metastatic ALK-rearranged lung adenocarcinoma treated with alectinib throughout pregnancy. Notably, the patient had two uncomplicated pregnancies with routine obstetrical and postnatal courses. In this case, alectinib did not seem to affect embryofetal or early childhood development. This does not exclude undetectable or delayed toxic effects, and additional studies are needed to further reveal the safety of alectinib treatment during pregnancy.

Systemic Lupus Erythematosus Delivery Outcomes Are Unchanged Across Three Decades

Objective

Using a large, de‐identified electronic health record database with over 3.2 million patients, we aimed to identify trends of systemic lupus erythematosus (SLE) medication use during pregnancy and birth outcomes from 1989 to 2020.

Methods

Using a previously validated algorithm for SLE deliveries, we identified 255 pregnancies in patients with SLE and 604 pregnancies in controls with no known autoimmune diseases. We examined demographics, medications, SLE comorbidities, and maternal and fetal outcomes in SLE and control deliveries.

Results

Compared with control deliveries, SLE deliveries were more likely to be complicated by preterm delivery (odds ratio [OR]: 6.71; 95% confidence interval [CI]: 4.31‐10.55; P < 0.001) and preeclampsia (OR: 3.22; 95% CI: 1.83‐5.66; P < 0.001) after adjusting for age at delivery, race, and parity. In a longitudinal analysis, medication use during SLE pregnancies remained relatively stable, with some increased use of hydroxychloroquine over time but no increase in aspirin use. For SLE deliveries, preterm delivery and preeclampsia rates remained stable.

Conclusion

We observed rates of preeclampsia and preterm delivery in SLE that were five times higher than the general population and higher compared with other prospective SLE cohorts. Furthermore, we did not observe improved outcomes over time with preeclampsia and preterm delivery. Despite increasing evidence for universal use of hydroxychloroquine and aspirin, we did not observe substantially higher use of these medications over time, particularly for aspirin. Our results demonstrate the continued need to prioritize educational and implementation efforts to improve adverse pregnancy outcomes in SLE.

Telehealth Use in the COVID-19 Pandemic: A Retrospective Study of Prenatal Care

Telehealth is an alternative care delivery model to in-person care. It uses electronic information and telecommunication technologies to provide remote clinical care to patients, especially those living in rural areas that lack sufficient access to health care services. Like other areas of care affected by the COVID-19 pandemic, the prevalence of telehealth has increased in prenatal care. This study reports on telehealth use in prenatal care at a large academic medical center in Middle Tennessee, USA. We examine the electronic health records of over 2500 women to characterize 1) the volume of prenatal visits participating in telehealth, 2) disparities in obstetric patients using telehealth, and 3) the impact of telehealth use on obstetric outcomes, including duration of intrapartum hospital stays, preterm birth, Cesarean rate, and newborn birthweight. Our results show that telehealth mainly was used in the second and third trimesters, especially for consulting services. In addition, we found that certain demographics correlated with lower telehealth utilization, including patients who were under 26 years old, were Black and/or Hispanic, were on a state-sponsored health insurance program, and those who lived in urban areas. Furthermore, no significant differences were found on preterm birth and Cesarean between the patients who used telehealth in their prenatal care and those who did not.

Learning the impact of acute and chronic diseases on forecasting neonatal encephalopathy

OBJECTIVE

There is a wide range of risk factors predisposing to the onset of neonatal encephalopathy (NE), including maternal antepartum/intrapartum comorbidities or events. However, few studies have investigated the difference in the impact of acute and chronic diseases on forecasting NE, which could assist clinicians in choosing the best course of action to prevent NE or reduce its severity and complications. In this study, we aimed to engineer features based on acute and chronic diseases and assess the differences of the impact of acute and chronic diseases on NE prediction using machine learning models.

MATERIALS AND METHODS

We used ten years of electronic health records of mothers from a large academic medical center to develop three types of features: chronic disease, recurrence of an acute disease, and temporal relationships between acute diseases. Two types of NE prediction models, based on acute and chronic diseases, respectively, were trained with feature selection. We further compared the prediction performance of the models with two state-of-the-art NE forecasting models. The machine learning models ranked the three types of engineered features based on their contributions to the NE prediction.

RESULTS

The NE model trained on acute disease features showed significantly higher AUC than the model relying on chronic disease features (AUC difference: 0.161, p-value < 0.001). The NE model trained on both acute and chronic disease features achieved the highest average AUC (0.889), with a significant improvement over the best existing model (0.854) with p = 0.0129. Recurrence of "known or suspected fetal abnormality affecting management of mother (655)" was assigned the highest weights in predicting NE.

CONCLUSIONS

Machine learning models based on the three types of engineered features significantly improve NE prediction. Our results specifically suggest that acute disease-associated features play a more important role in predicting NE.

Association of Individual and Community Factors With Hepatitis C Infections Among Pregnant People and Newborns

Importance

The opioid crisis has increasingly affected pregnant people and infants. Hepatitis C virus (HCV) infections, a known complication of opioid use, grew in parallel with opioid-related complications; however, the literature informing individual and community risks associated with maternal HCV infection is sparse.

Objectives

To determine (1) individual and county-level factors associated with HCV among pregnant people and their newborn infants, and (2) how county-level factors influence individual risk among the highest risk individuals.

Design Setting and Participants

This time-series analysis of retrospective, repeated cross-sectional data included pregnant people in all US counties from 2009 to 2019. We constructed mixed-effects logistic regression models to explore the association between HCV infection and individual and county-level covariates. Analyses were conducted between June 2019 and September 2021.

Exposures

Individual-level: race and ethnicity, education, marital status, insurance type; county-level: rurality, employment, density of obstetricians.

Main Outcomes and Measures

Hepatitis C virus as indicated on the newborn's birth certificate.

Results

Between 2009 and 2019, there were 39 380 122 pregnant people who met inclusion criteria, among whom 138 343 (0.4%) were diagnosed with HCV. People with HCV were more likely to be White (79.9% vs 53.5%), American Indian or Alaska Native (AI/AN) (2.9% vs 0.9%), be without a 4-year degree (93.2% vs 68.6%), and be unmarried (73.7% vs 38.8%). The rate (per 1000 live births) of HCV among pregnant people increased from 1.8 to 5.1. In adjusted analyses, the following factors were associated with higher rates of HCV: individuals identified as White (adjusted odds ratio [aOR], 7.37; 95% CI, 7.20-7.55) and AI/AN (aOR, 7.94; 95% CI, 7.58-8.31) compared with Black individuals, those without a 4-year degree (aOR, 3.19; 95% CI, 3.11-3.28), those with Medicaid vs private insurance (aOR, 3.27; 95% CI, 3.21-3.33), and those who were unmarried (aOR, 2.80; 95% CI, 2.76-2.84); whereas, rural residence, higher rates of employment, and greater density of obstetricians was associated with lower risk of HCV. Among individuals at the highest risk of HCV, higher levels of county employment, accounting for other factors, were associated with less of a rise in HCV infections over time.

Conclusions and Relevance

In this cross-sectional study, maternal and newborn HCV infections increased substantially between 2009 and 2019, disproportionately among White and AI/AN people without a 4-year degree. County-level factors, including higher levels of employment, were associated with lower individual risks of acquiring the virus.

Society for Maternal-Fetal Medicine Consult Series #50: The role of activity restriction in obstetric management: (Replaces Consult Number 33, August 2014)

Despite current recommendations against its use, activity restriction remains a common intervention used to prevent preterm birth in multiple clinical settings. Hypertensive disorders of pregnancy, preterm premature rupture of membranes, multiple gestations, vaginal bleeding, short cervical length, placenta previa, and fetal growth restriction are also common reasons for antepartum hospital admission and frequently lead to a recommendation for activity restriction. However, numerous reports have shown that activity restriction does not prevent adverse obstetrical outcomes but does confer significant physical and psychosocial risks. This consult reviews the current literature on activity restriction and examines the evidence regarding its use in obstetrical management. The recommendations by the Society for Maternal-Fetal Medicine are as follows: (1) we recommend against the routine use of any type of activity restriction in pregnant women at risk of preterm birth based on preterm labor symptoms, arrested preterm labor, or shortened cervix (GRADE 1B); (2) we recommend against the use of routine inpatient hospitalization and activity restriction for the prevention of preterm birth in women with multiple gestations (GRADE 1A); and (3) given the lack of data definitively demonstrating that activity restriction improves perinatal outcome in pregnancies complicated by fetal growth restriction, preterm premature rupture of membranes, or hypertensive diseases of pregnancy, coupled with evidence of adverse effects of activity restriction, we suggest that activity restriction not be prescribed for the treatment of pregnancies complicated by fetal growth restriction, preterm premature rupture of membranes, or hypertensive disease (GRADE 2B).

Deep learning predicts extreme preterm birth from electronic health records

OBJECTIVE

Models for predicting preterm birth generally have focused on very preterm (28-32 weeks) and moderate to late preterm (32-37 weeks) settings. However, extreme preterm birth (EPB), before the 28th week of gestational age, accounts for the majority of newborn deaths. We investigated the extent to which deep learning models that consider temporal relations documented in electronic health records (EHRs) can predict EPB.

STUDY DESIGN

EHR data were subject to word embedding and a temporal deep learning model, in the form of recurrent neural networks (RNNs) to predict EPB. Due to the low prevalence of EPB, the models were trained on datasets where controls were undersampled to balance the case-control ratio. We then applied an ensemble approach to group the trained models to predict EPB in an evaluation setting with a nature EPB ratio. We evaluated the RNN ensemble models with 10 years of EHR data from 25,689 deliveries at Vanderbilt University Medical Center. We compared their performance with traditional machine learning models (logistical regression, support vector machine, gradient boosting) trained on the datasets with balanced and natural EPB ratio. Risk factors associated with EPB were identified using an adjusted odds ratio.

RESULTS

The RNN ensemble models trained on artificially balanced data achieved a higher AUC (0.827 vs. 0.744) and sensitivity (0.965 vs. 0.682) than those RNN models trained on the datasets with naturally imbalanced EPB ratio. In addition, the AUC (0.827) and sensitivity (0.965) of the RNN ensemble models were better than the AUC (0.777) and sensitivity (0.819) of the best baseline models trained on balanced data. Also, risk factors, including twin pregnancy, short cervical length, hypertensive disorder, systemic lupus erythematosus, and hydroxychloroquine sulfate, were found to be associated with EPB at a significant level.

CONCLUSION

Temporal deep learning can predict EPB up to 8 weeks earlier than its occurrence. Accurate prediction of EPB may allow healthcare organizations to allocate resources effectively and ensure patients receive appropriate care.

Learning to Identify Severe Maternal Morbidity from Electronic Health Records

Severe maternal morbidity (SMM) is broadly defined as significant complications in pregnancy that have an adverse effect on women's health. Identifying women who experience SMM and reviewing their obstetric care can assist healthcare organizations in recognizing risk factors and best practices for management. Various definitions of SMM have been posited, but there is no consensus. Existing definitions are further limited in that they 1) are often rooted in existing clinical knowledge (which is problematic as many risk factors remain unknown), leading to poor positive predictive performance (PPV), and 2) have limited scalability as they often require substantial chart review. Thus, in this paper, a machine learning framework was introduced to automatically identify SMM and relevant risk factors from electronic health records (EHRs). We evaluated this framework with EHR data from 45,858 deliveries at a large academic medical center. The framework outperformed a state-of-the-art model from the U.S. Centers for Disease Control and Prevention (AUC of 0.94 vs. 0.80). Specially, it improved upon PPV by 59% (CDC: 0.22 vs. our model: 0.35). In the process, we revealed several novel SMM indicators, including disorders of fluid or electrolytes, systemic inflammatory response syndrome, and acidosis.

Leveraging Electronic Health Records to Learn Progression Path for Severe Maternal Morbidity

Severe maternal morbidity (SMM) encompasses a wide range of serious health complications that would likely result in death without in-time medical attention. It has been recognized that various demographic factors (e.g., age and race) and medical conditions (e.g., preeclampsia and organ failure) are associated with SMM. However, how medical conditions develop into SMM is seldom investigated. We hypothesize that SMM has a progression path, which is associated with a sequence of risk factors rather than a set of independent individual factors. We implemented a data-driven framework that leverages electronic health records (EHRs) in the antepartum period to learn the temporal patterns and measure their relationships with SMM during the delivery hospitalization. We evaluate the framework with two years of data from 6,184 women who had delivery hospitalizations at Vanderbilt University Medical Center. We discovered 69 temporal patterns, 12 of which were confirmed to be significantly associated with SMM.

Obstetric Patients with Repetitious Hospital Location Transfers Have Prolonged Stays

There is a general belief that the workflow of surrounding location transfers between locations documented in electronic health record (EHR) during hospitalization is associated with a patient's length of stay (LOS). However, this belief has had little formal investigation in a data-driven manner. Location transfers in patients' hospitalization are hypothesized to be related to LOS. The objective of this study is to assess this relationship, using data derived from the EHR system of a large hospital system, with a focus on the obstetric setting - a clinical environment that exhibits wide swing in resource utilization. We designed a data-driven framework to infer patterns of location transfers and developed a zero-truncated negative binomial model, adjusting for demographics and billed diagnoses, to learn the association between patterns of location transfers and LOS. Indicative factors found to be of indicative of location transfer patterns were further investigated via their odds ratios, Pearson Correlation Coefficients, and Chi-squared test. We evaluated our approach with two years of data on from 5,774 obstetric inpatient encounters from the EHR system of Northwestern Memorial Hospital. The results indicated that the average LOS for patients with patterns of repetitious location transfers (RLTs) was 4.25 days (95% confidence interval [4.02, 4.47]) longer than patients with no RLT. This difference reduced to 3.62 days (95% confidence interval [3.61, 3.64]) after adjusting for age, race and billed diagnoses. We further discovered 21 indicative factors of RLT (statistically significant with a significance level of 0.05), in the form of billed diagnosis codes, each of which exhibited an odds ratio larger than 4. This study suggests that RLT patterns are associated with a prolonged LOS in the obstetric setting. As such, healthcare organizations may need to pay more attention to patients with RLTs to refine location transfers workflow and to boost efficiency in obstetric care.

Predicting Neonatal Encephalopathy From Maternal Data in Electronic Medical Records

Neonatal encephalopathy (NE) is a leading cause of neonatal mortality and lifetime neurological disability. The earlier the risk of NE can be assessed, the more effective interventions can be in preventing adverse outcomes. Existing studies that focus on intrapartum risk factors do not provide the early prognostic forecasting necessary to prepare healthcare professionals to intervene early in a high-risk NE case. This work used maternal data in a supervised machine learning framework to predict NE events. Specifically, we 1) collected the electronic medical records (EMRs) for 104 NE newborns and 31,054 non-NE newborns and their mothers, 2) trained and tested a regularized logistic regression on imbalanced and high-dimensional EMR data, and 3) discerned important features that could be possible risk factors. The learned model offers prenatal predictions of NE cases with an average area under the receiving operator characteristic curve (AUC) of 87% and identified the most important predictors.

Learning bundled care opportunities from electronic medical records

OBJECTIVE

The traditional fee-for-service approach to healthcare can lead to the management of a patient's conditions in a siloed manner, inducing various negative consequences. It has been recognized that a bundled approach to healthcare - one that manages a collection of health conditions together - may enable greater efficacy and cost savings. However, it is not always evident which sets of conditions should be managed in a bundled manner. In this study, we investigate if a data-driven approach can automatically learn potential bundles.

METHODS

We designed a framework to infer health condition collections (HCCs) based on the similarity of their clinical workflows, according to electronic medical record (EMR) utilization. We evaluated the framework with data from over 16,500 inpatient stays from Northwestern Memorial Hospital in Chicago, Illinois. The plausibility of the inferred HCCs for bundled care was assessed through an online survey of a panel of five experts, whose responses were analyzed via an analysis of variance (ANOVA) at a 95% confidence level. We further assessed the face validity of the HCCs using evidence in the published literature.

RESULTS

The framework inferred four HCCs, indicative of (1) fetal abnormalities, (2) late pregnancies, (3) prostate problems, and (4) chronic diseases, with congestive heart failure featuring prominently. Each HCC was substantiated with evidence in the literature and was deemed plausible for bundled care by the experts at a statistically significant level.

CONCLUSIONS

The findings suggest that an automated EMR data-driven framework conducted can provide a basis for discovering bundled care opportunities. Still, translating such findings into actual care management will require further refinement, implementation, and evaluation.

Predicting Length of Stay for Obstetric Patients via Electronic Medical Records

Obstetric care refers to the care provided to patients during ante-, intra-, and postpartum periods. Predicting length of stay (LOS) for these patients during their hospitalizations can assist healthcare organizations in allocating hospital resources more effectively and efficiently, ultimately improving maternal care quality and reducing costs to patients. In this paper, we investigate the extent to which LOS can be forecast from a patient's medical history. We introduce a machine learning framework to incorporate a patient's prior conditions (e.g., diagnostic codes) as features in a predictive model for LOS. We evaluate the framework with three years of historical billing data from the electronic medical records of 9188 obstetric patients in a large academic medical center. The results indicate that our framework achieved an average accuracy of 49.3%, which is higher than the baseline accuracy 37.7% (that relies solely on a patient's age). The most predictive features were found to have statistically significant discriminative ability. These features included billing codes for normal delivery (indicative of shorter stay) and antepartum hypertension (indicative of longer stay).

Racial/Ethnic Differences in Gestational Diabetes Prevalence and Contribution of Common Risk Factors

BACKGROUND

The White House, the American Heart Association, the Agency for Healthcare Research and Quality, and the National Heart, Lung and Blood Institute have all recently acknowledged the need to disaggregate Asian American subgroups to better understand this heterogeneous racial group. This study aims to assess racial/ethnic differences in relative contribution of risk factors of gestational diabetes mellitus (GDM) among Asian subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, and Vietnamese), Hispanics, non-Hispanic blacks, and non-Hispanic whites.

METHODS

Pregnant women in 2007-2012 were identified through California state birth certificate records and linked to the electronic health records in a large mixed-payer ambulatory care organisation in Northern California (n = 24 195). Relative risk and population attributable fraction (PAF) for specific racial/ethnic groups were calculated to assess the contributions of advanced maternal age, overweight/obesity (Centers for Disease Control and Prevention (CDC) standards and World Health Organization (WHO)/American Diabetes Association (ADA) body mass index cut-offs for Asians), family history of type 2 diabetes, and foreign-born status.

RESULTS

GDM was most prevalent among Asian Indians (19.3%). Relative risks were similar across all race/ethnic groups. Advanced maternal age had higher PAFs in non-Hispanic whites (22.5%) and Hispanics (22.7%). Meanwhile family history (Asian Indians 22.6%, Chinese 22.9%) and foreign-borne status (Chinese 40.2%, Filipinos 30.2%) had higher PAFs in Asian subgroups. Overweight/obesity was the most important GDM risk factor for non-Hispanic whites, Hispanics, Asian Indians, and Filipinos when the WHO/ADA cut-off points were applied. Advanced maternal age was the only risk factor studied that was modified by race/ethnicity, with non-Hispanic white and Hispanic women being more adversely affected than other racial/ethnic groups.

CONCLUSIONS

Overweight/obesity, advanced maternal age, family history of type 2 diabetes, and foreign-borne status are important risk factors for GDM. The relative contributions of these risk factors differ by race/ethnicity, mainly due to differences in population prevalence of these risk factors.