Correction: Publicly available data reveals association between asthma hospitalizations and unconventional natural gas development in Pennsylvania.

[This corrects the article DOI: 10.1371/journal.pone.0265513.].

Development and Optimization of Machine Learning Algorithms for Predicting In-hospital Patient Charges for Congestive Heart Failure Exacerbations, Chronic Obstructive Pulmonary Disease Exacerbations and Diabetic Ketoacidosis.

Background: Hospitalizations for exacerbations of congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD) and diabetic ketoacidosis (DKA) are costly in the United States. The purpose of this study was to predict in-hospital charges for each condition using machine learning (ML) models. Results: We conducted a retrospective cohort study on national discharge records of hospitalized adult patients from January 1st, 2016, to December 31st, 2019. We used numerous ML techniques to predict in-hospital total cost. We found that linear regression (LM), gradient boosting (GBM) and extreme gradient boosting (XGB) models had good predictive performance and were statistically equivalent, with training R-square values ranging from 0.49-0.95 for CHF, 0.56-0.95 for COPD, and 0.32-0.99 for DKA. We identified important key features driving costs, including patient age, length of stay, number of procedures. and elective/nonelective admission. Conclusions: ML methods may be used to accurately predict costs and identify drivers of high cost for COPD exacerbations, CHF exacerbations and DKA. Overall, our findings may inform future studies that seek to decrease the underlying high patient costs for these conditions.

Comparison of different definitions of traumatic brain injury: implications for cohort characteristics and survival in women, Philadelphia, USA.

BACKGROUND: Traumatic brain injury (TBI) is an acute injury that is understudied in civilian cohorts, especially among women, as TBI has historically been considered to be largely a condition of athletes and military service people. Both the Centres for Disease Control and Prevention (CDC) and Department of Defense (DOD)/Veterans Affairs (VA) have developed case definitions to identify patients with TBI from medical records; however, their definitions differ. We sought to re-examine these definitions to construct an expansive and more inclusive definition among a cohort of women with TBI. METHODS: In this study, we use electronic health records (EHR) from a single healthcare system to study the impact of using different case definitions to identify patients with TBI. Specifically, we identified adult female patients with TBI using the CDC definition, DOD/VA definition and a combined and expanded definition herein called the Penn definition. RESULTS: We identified 4446 adult-female TBI patients meeting the CDC definition, 3619 meeting the DOD/VA definition, and together, 6432 meeting our expanded Penn definition that includes the CDC ad DOD/VA definitions. CONCLUSIONS: Using the expanded definition identified almost two times as many patients, enabling investigations to more fully characterise these patients and related outcomes. Our expanded TBI case definition is available to other researchers interested in employing EHRs to investigate TBI.

Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records.

STUDY OBJECTIVES: The objectives of this study were to understand the relative comorbidity burden of obstructive sleep apnea (OSA), determine whether these relationships were modified by sex or age, and identify patient subtypes defined by common comorbidities. METHODS: Cases with OSA and noncases (controls) were defined using a validated electronic health record (EHR)-based phenotype and matched for age, sex, and time period of follow-up in the EHR. We compared prevalence of the 20 most common comorbidities between matched cases and controls using conditional logistic regression with and without controlling for body mass index. Latent class analysis was used to identify subtypes of OSA cases defined by combinations of these comorbidities. RESULTS: In total, 60,586 OSA cases were matched to 60,586 controls (from 1,226,755 total controls). Patients with OSA were more likely to have each of the 20 most common comorbidities compared with controls, with odds ratios ranging from 3.1 to 30.8 in the full matched set and 1.3 to 10.2 after body mass index adjustment. Associations between OSA and these comorbidities were generally stronger in females and patients with younger age at diagnosis. We identified 5 distinct subgroups based on EHR-defined comorbidities: High Comorbidity Burden, Low Comorbidity Burden, Cardiovascular Comorbidities, Inflammatory Conditions and Less Obesity, and Inflammatory Conditions and Obesity. CONCLUSIONS: Our study demonstrates the power of leveraging the EHR to understand the relative health burden of OSA, as well as heterogeneity in these relationships based on age and sex. In addition to enrichment for comorbidities, we identified 5 novel OSA subtypes defined by combinations of comorbidities in the EHR, which may be informative for understanding disease outcomes and improving prevention and clinical care. Overall, this study adds more evidence that OSA is heterogeneous and requires personalized management. CITATION: Te TT, Keenan BT, Veatch OJ, Boland MR, Hubbard RA, Pack AI. Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records. J Clin Sleep Med. 2024;20(4):521-533.

Investigating Three Classification Methods for Per/Poly-Fluoroalkyl Substance (PFAS) Exposure from Electronic Health Records And Potential for Bias.

Per-/poly-fluoroalkyl substances (PFAS) are a group of manmade compounds with known human toxicity and evidence of contamination in drinking water throughout the US. We augmented our electronic health record data with geospatial information to classify PFAS exposure for our patients living in New Jersey. We explored the utility of three different methods for classifying PFAS exposure that are popularly used in the literature, resulting in different boundary types: public water supplier service area boundary, municipality, and ZIP code. We also explored the intersection of the three boundaries. To study the potential for bias, we investigated known PFAS exposure-disease associations, specifically hypertension, thyroid disease and parathyroid disease. We found that both the significance of the associations and the effect size varied by the method for classifying PFAS exposure. This has important implications in knowledge discovery and also environmental justice as across cohorts, we found a larger proportion of Black/African-American patients PFAS-exposed.

Assessing racial residential segregation as a risk factor for severe maternal morbidity.

PURPOSE: To measure associations of area-level racial and economic residential segregation with severe maternal morbidity (SMM). METHODS: We conducted a retrospective cohort study of births at two Philadelphia hospitals between 2018 and 2020 to analyze associations of segregation, quantified using the Index of Concentration at the Extremes (ICE), with SMM. We used stratified multivariable, multilevel, logistic regression models to determine whether associations of ICE with SMM varied by self-identified race or hospital catchment. RESULTS: Of the 25,979 patients (44.1% Black, 35.8% White), 1381 (5.3%) had SMM (Black [6.1%], White [4.4%]). SMM was higher among patients residing outside (6.3%), than inside (5.0%) Philadelphia (P < .001). Overall, ICE was not associated with SMM. However, ICErace (higher proportion of White vs. Black households) was associated with lower odds of SMM among patients residing inside Philadelphia (aOR 0.87, 95% CI: 0.80-0.94) and higher odds outside Philadelphia (aOR 1.12, 95% CI: 0.95-1.31). Moran's I indicated spatial autocorrelation of SMM overall (P < .001); when stratified, autocorrelation was only evident outside Philadelphia. CONCLUSIONS: Overall, ICE was not associated with SMM. However, higher ICErace was associated with lower odds of SMM among Philadelphia residents. Findings highlight the importance of hospital catchment area and referral patterns in spatial analyses of hospital datasets.

Risk of severe acute respiratory syndrome coronavirus 2 infection among women with polycystic ovary syndrome.

OBJECTIVE: To determine whether women with polycystic ovary syndrome (PCOS) had a higher incidence of testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) than those without PCOS and evaluate whether PCOS diagnosis independently increased the risk of moderate or severe disease in those with positive SARS-CoV-2 test results. DESIGN: Retrospective cohort study using the National COVID Cohort Collaborative (N3C). SETTING: National COVID Cohort Collaborative. PATIENT(S): Adult nonpregnant women (age, 18-65 years) enrolled in the N3C with confirmed SARS-CoV-2 testing for any indication. Sensitivity analyses were conducted in women aged 18-49 years and who were obese (body mass index, ≥30 kg/m2). INTERVENTION(S): The exposure was PCOS as identified by the N3C clinical diagnosis codes and concept sets, which are a compilation of terms, laboratory values, and International Classification of Diseases codes for the diagnosis of PCOS. To further capture patients with the symptoms of PCOS, we also included those who had concept sets for both hirsutism and irregular menses. MAIN OUTCOME MEASURE(S): Odds of testing positive for SARS-CoV-2 and odds of moderate or severe coronavirus disease 2019 (COVID-19) in the PCOS cohort compared with those in the non-PCOS cohort. RESULT(S): Of the 2,089,913 women included in our study, 39,459 had PCOS. In the overall cohort, the adjusted odds ratio (aOR) of SARS-CoV-2 positivity was 0.98 (95% confidence interval [CI], 0.97-0.98) in women with PCOS compared to women without PCOS. The aORs of disease severity were as follows: mild disease, 1.02 (95% CI, 1.01-1.03); moderate disease, 0.99 (95% CI, 0.98-1.00); and severe disease, 0.99 (95% CI, 0.99-1.00). There was no difference in COVID-19-related mortality (aOR, 1.00; 95% CI, 0.99-1.00). These findings were similar in the reproductive-age and obese reproductive-age cohorts. CONCLUSION(S): Women with PCOS had a similar likelihood of testing positive for SARS-CoV-2. Among those who tested positive, they were no more likely to have moderate or severe COVID-19 than the non-PCOS cohort. Polycystic ovary syndrome is a chronic condition associated with several comorbidities, including cardiovascular disease and mental health issues. Although these comorbidities are also associated with COVID-19 morbidity, our findings suggest that the comorbidities themselves, rather than PCOS, drive the risk of disease severity.

A deep learning method to detect opioid prescription and opioid use disorder from electronic health records.

OBJECTIVE: As the opioid epidemic continues across the United States, methods are needed to accurately and quickly identify patients at risk for opioid use disorder (OUD). The purpose of this study is to develop two predictive algorithms: one to predict opioid prescription and one to predict OUD. MATERIALS AND METHODS: We developed an informatics algorithm that trains two deep learning models over patient Electronic Health Records (EHRs) using the MIMIC-III database. We utilize both the structured and unstructured parts of the EHR and show that it is possible to predict both challenging outcomes. RESULTS: Our deep learning models incorporate elements from EHRs to predict opioid prescription with an F1-score of 0.88 ± 0.003 and an AUC-ROC of 0.93 ± 0.002. We also constructed a model to predict OUD diagnosis achieving an F1-score of 0.82 ± 0.05 and AUC-ROC of 0.94 ± 0.008. DISCUSSION: Our model for OUD prediction outperformed prior algorithms for specificity, F1 score and AUC-ROC while achieving equivalent sensitivity. This demonstrates the importance of a) deep learning approaches in predicting OUD and b) incorporating both structured and unstructured data for this prediction task. No prediction models for opioid prescription as an outcome were found in the literature and therefore our model is the first to predict opioid prescribing behavior. CONCLUSION: Algorithms such as those described in this paper will become increasingly important to understand the drivers underlying this national epidemic.

A medication-wide association study (MWAS) on repurposed drugs for COVID-19 with Pre-pandemic prescription medication exposure and pregnancy outcomes.

Information on effects of medication therapies during pregnancy is lacking as pregnant patients are often excluded from clinical trials. This retrospective study explores the potential of using electronic health record (EHR) data to inform safety profiles of repurposed COVID medication therapies on pregnancy outcomes using pre-COVID data. We conducted a medication-wide association study (MWAS) on prescription medication exposures during pregnancy and the risk of cesarean section, preterm birth, and stillbirth, using EHR data between 2010-2017 on deliveries at PennMedicine. Repurposed drugs studied for treatment of COVID-19 were extracted from ClinicalTrials.gov (n = 138). We adjusted for known comorbidities diagnosed within 2 years prior to birth. Using previously developed medication mapping and delivery-identification algorithms, we identified medication exposure in 2,830 of a total 63,334 deliveries; from 138 trials, we found 31 medications prescribed and included in our cohort. We found 21 (68%) of the 31 medications were not positively associated with increased risk of the outcomes examined. With caution, these medications warrant potential for inclusion of pregnant individuals in future studies, while drugs found to be associated with pregnancy outcomes require further investigation. MWAS facilitates hypothesis-driven evaluation of drug safety across all prescription medications, revealing potential drug candidates for further research.

Medication-Wide Association Study Using Electronic Health Record Data of Prescription Medication Exposure and Multifetal Pregnancies: Retrospective Study.

BACKGROUND: Medication-wide association studies (MWAS) have been applied to assess the risk of individual prescription use and a wide range of health outcomes, including cancer, acute myocardial infarction, acute liver failure, acute renal failure, and upper gastrointestinal ulcers. Current literature on the use of preconception and periconception medication and its association with the risk of multiple gestation pregnancies (eg, monozygotic and dizygotic) is largely based on assisted reproductive technology (ART) cohorts. However, among non-ART pregnancies, it is unknown whether other medications increase the risk of multifetal pregnancies. OBJECTIVE: This study aimed to investigate the risk of multiple gestational births (eg, twins and triplets) following preconception and periconception exposure to prescription medications in patients who delivered at Penn Medicine. METHODS: We used electronic health record data between 2010 and 2017 on patients who delivered babies at Penn Medicine, a health care system in the Greater Philadelphia area. We explored 3 logistic regression models: model 1 (no adjustment); model 2 (adjustment for maternal age); and model 3-our final logistic regression model (adjustment for maternal age, ART use, and infertility diagnosis). In all models, multiple births (MBs) were our outcome of interest (binary outcome), and each medication was assessed separately as a binary variable. To assess our MWAS model performance, we defined ART medications as our gold standard, given that these medications are known to increase the risk of MB. RESULTS: Of the 63,334 distinct deliveries in our cohort, only 1877 pregnancies (2.96%) were prescribed any medication during the preconception and first trimester period. Of the 123 medications prescribed, we found 26 (21.1%) medications associated with MB (using nominal P values) and 10 (8.1%) medications associated with MB (using Bonferroni adjustment) in fully adjusted model 3. We found that our model 3 algorithm had an accuracy of 85% (using nominal P values) and 89% (using Bonferroni-adjusted P values). CONCLUSIONS: Our work demonstrates the opportunities in applying the MWAS approach with electronic health record data to explore associations between preconception and periconception medication exposure and the risk of MB while identifying novel candidate medications for further study. Overall, we found 3 novel medications linked with MB that could be explored in further work; this demonstrates the potential of our method to be used for hypothesis generation.

An algorithm to identify residential mobility from electronic health-record data.

BACKGROUND: Environmental, social and economic exposures can be inferred from address information recorded in an electronic health record. However, these data often contain administrative errors and misspellings. These issues make it challenging to determine whether a patient has moved, which is integral for accurate exposure assessment. We aim to develop an algorithm to identify residential mobility events and avoid exposure misclassification. METHODS: At Penn Medicine, we obtained a cohort of 12 147 pregnant patients who delivered between 2013 and 2017. From this cohort, we identified 9959 pregnant patients with address information at both time of delivery and one year prior. We developed an algorithm entitled REMAP (Relocation Event Moving Algorithm for Patients) to identify residential mobility during pregnancy and compared it to using ZIP code differences alone. We assigned an area-deprivation exposure score to each address and assessed how residential mobility changed the deprivation scores. RESULTS: To assess the accuracy of our REMAP algorithm, we manually reviewed 3362 addresses and found that REMAP was 95.7% accurate. In this large urban cohort, 41% of patients moved during pregnancy. REMAP outperformed the comparison of ZIP codes alone (82.9%). If residential mobility had not been taken into account, absolute area deprivation would have misclassified 39% of the patients. When setting a threshold of one quartile for misclassification, 24.4% of patients would have been misclassified. CONCLUSIONS: Our study tackles an important characterization problem for exposures that are assigned based upon residential addresses. We demonstrate that methods using ZIP code alone are not adequate. REMAP allows address information from electronic health records to be used for accurate exposure assessment and the determination of residential mobility, giving researchers and policy makers more reliable information.

Neighborhood deprivation increases the risk of Post-induction cesarean delivery.

OBJECTIVE: The purpose of this study was to measure the association between neighborhood deprivation and cesarean delivery following labor induction among people delivering at term (≥37 weeks of gestation). MATERIALS AND METHODS: We conducted a retrospective cohort study of people ≥37 weeks of gestation, with a live, singleton gestation, who underwent labor induction from 2010 to 2017 at Penn Medicine. We excluded people with a prior cesarean delivery and those with missing geocoding information. Our primary exposure was a nationally validated Area Deprivation Index with scores ranging from 1 to 100 (least to most deprived). We used a generalized linear mixed model to calculate the odds of postinduction cesarean delivery among people in 4 equally-spaced levels of neighborhood deprivation. We also conducted a sensitivity analysis with residential mobility. RESULTS: Our cohort contained 8672 people receiving an induction at Penn Medicine. After adjustment for confounders, we found that people living in the most deprived neighborhoods were at a 29% increased risk of post-induction cesarean delivery (adjusted odds ratio = 1.29, 95% confidence interval, 1.05-1.57) compared to the least deprived. In a sensitivity analysis, including residential mobility seemed to magnify the effect sizes of the association between neighborhood deprivation and postinduction cesarean delivery, but this information was only available for a subset of people. CONCLUSIONS: People living in neighborhoods with higher deprivation had higher odds of postinduction cesarean delivery compared to people living in less deprived neighborhoods. This work represents an important first step in understanding the impact of disadvantaged neighborhoods on adverse delivery outcomes.

Evaluation of Stillbirth Among Pregnant People With Sickle Cell Trait.

Importance: Relative to what is known about pregnancy complications and sickle cell disease (SCD), little is known about the risk of pregnancy complications among those with sickle cell trait (SCT). There is a lack of clinical research among sickle cell carriers largely due to low sample sizes and disparities in research funding. Objective: To evaluate whether there is an association between SCT and a stillbirth outcome. Design, Setting, and Participants: This retrospective cohort study included data on deliveries occurring between January 1, 2010, and August 15, 2017, at 4 quaternary academic medical centers within the Penn Medicine health system in Pennsylvania. The population included a total of 2482 deliveries from 1904 patients with SCT but not SCD, and 215 deliveries from 164 patients with SCD. Data were analyzed from May 3, 2019, to September 16, 2021. Exposures: The primary exposure of interest was SCT, identified using clinical diagnosis codes recorded in the electronic health record. Main Outcomes and Measures: A multivariate logistic regression model was constructed to assess the risk of stillbirth using the following risk factors: SCD, numbers of pain crises and blood transfusions before delivery, delivery episode (as a proxy for parity), prior cesarean delivery, multiple gestation, patient age, marital status, race and ethnicity, ABO blood type, Rhesus (Rh) factor, and year of delivery. Results: This cohort study included 50 560 patients (63 334 deliveries), most of whom were aged 25 to 34 years (29 387 of 50 560 [58.1%]; mean [SD] age, 29.5 [6.1] years), were single at the time of delivery (28 186 [55.8%]), were Black or African American (23 777 [47.0%]), had ABO blood type O (22 879 [45.2%]), and were Rhesus factor positive (44 000 [87.0%]). From this general population, 2068 patients (4.1%) with a sickle cell gene variation were identified: 1904 patients (92.1%) with SCT (2482 deliveries) and 164 patients (7.9%) with SCD (215 deliveries). In the fully adjusted model, SCT was associated with an increased risk of stillbirth (adjusted odds ratio [aOR], 8.94; 95% CI, 1.05-75.79; P = .045) while adjusting for the risk factors of SCD (aOR, 26.40; 95% CI, 2.48-280.90; P = .007) and multiple gestation (aOR, 4.68; 95% CI, 3.48-6.29; P < .001). Conclusions and Relevance: The results of this large, retrospective cohort study indicate an increased risk of stillbirth among pregnant people with SCT. These findings underscore the need for additional risk assessment during pregnancy for sickle cell carriers.

Association of Neighborhood-Level Factors and COVID-19 Infection Patterns in Philadelphia Using Spatial Regression.

As of August 2020, there were ~6 million COVID-19 cases in the United States of America, resulting in ~200,000 deaths. Informatics approaches are needed to better understand the role of individual and community risk factors for COVID-19. We developed an informatics method to integrate SARS-CoV-2 data with multiple neighborhood-level factors from the American Community Survey and opendataphilly.org. We assessed the spatial association between neighborhood-level factors and the frequency of SARS-CoV-2 positivity, separately across all patients and across asymptomatic patients. We found that neighborhoods with higher proportions of individuals with a high-school degree and/or who were identified as Hispanic/Latinx were more likely to have higher SARS-CoV-2 positivity rates, after adjusting for other neighborhood covariates. Patients from neighborhoods with higher proportions of individuals receiving public assistance and/or identified as White were less likely to test positive for SARS-CoV-2. Our approach and its findings could inform future public health efforts.

Development of an Informatics Algorithm to Link Seasonal Infectious Diseases to Birth-Dependent Diseases Across Species: A Case Study with Osteosarcoma.

Many diseases have been linked with birth seasonality, and these fall into four main categories: mental, cardiovascular, respiratory and women's reproductive health conditions. Informatics methods are needed to uncover seasonally varying infectious diseases that may be responsible for the increased birth month-dependent disease risk observed. We have developed a method to link seasonal infectious disease data from the USA to birth month dependent disease data from humans and canines. We also include seasonal air pollution and climate data to determine the seasonal factors most likely involved in the response. We test our method with osteosarcoma, a rare bone cancer. We found the Lyme disease incidence was the most strongly correlated significant factor in explaining the birth month-osteosarcoma disease pattern (R=0.418, p=2.80X10-23), and this was true across all populations observed: canines, pediatric, and adult populations.

Harnessing electronic health records to study emerging environmental disasters: a proof of concept with perfluoroalkyl substances (PFAS).

Environmental disasters are anthropogenic catastrophic events that affect health. Famous disasters include the Seveso disaster and the Fukushima-Daiichi nuclear meltdown, which had disastrous health consequences. Traditional methods for studying environmental disasters are costly and time-intensive. We propose the use of electronic health records (EHR) and informatics methods to study the health effects of emergent environmental disasters in a cost-effective manner. An emergent environmental disaster is exposure to perfluoroalkyl substances (PFAS) in the Philadelphia area. Penn Medicine (PennMed) comprises multiple hospitals and facilities within the Philadelphia Metropolitan area, including over three thousand PFAS-exposed women living in one of the highest PFAS exposure areas nationwide. We developed a high-throughput method that utilizes only EHR data to evaluate the disease risk in this heavily exposed population. We replicated all five disease/conditions implicated by PFAS exposure, including hypercholesterolemia, thyroid disease, proteinuria, kidney disease and colitis, either directly or via closely related diagnoses. Using EHRs coupled with informatics enables the health impacts of environmental disasters to be more easily studied in large cohorts versus traditional methods that rely on interviews and expensive serum-based testing. By reducing cost and increasing the diversity of individuals included in studies, we can overcome many of the hurdles faced by previous studies, including a lack of racial and ethnic diversity. This proof-of-concept study confirms that EHRs can be used to study human health and disease impacts of environmental disasters and produces equivalent disease-exposure knowledge to prospective epidemiology studies while remaining cost-effective.

Individual-Level and Neighborhood-Level Risk Factors for Severe Maternal Morbidity.

OBJECTIVE: To investigate the association between individual-level and neighborhood-level risk factors and severe maternal morbidity. METHODS: This was a retrospective cohort study of all pregnancies delivered between 2010 and 2017 in the University of Pennsylvania Health System. International Classification of Diseases codes classified severe maternal morbidity according to the Centers for Disease Control and Prevention guidelines. Logistic regression modeling evaluated individual-level risk factors for severe maternal morbidity, such as maternal age and preeclampsia diagnosis. Additionally, we used spatial autoregressive modeling to assess Census-tract, neighborhood-level risk factors for severe maternal morbidity such as violent crime and poverty. RESULTS: Overall, 63,334 pregnancies were included, with a severe maternal morbidity rate of 2.73%, or 272 deliveries with severe maternal morbidity per 10,000 delivery hospitalizations. In our multivariable model assessing individual-level risk factors for severe maternal morbidity, the magnitude of risk was highest for patients with a cesarean delivery (adjusted odds ratio [aOR] 3.50, 95% CI 3.15-3.89), stillbirth (aOR 4.60, 95% CI 3.31-6.24), and preeclampsia diagnosis (aOR 2.71, 95% CI 2.41-3.03). Identifying as White was associated with lower odds of severe maternal morbidity at delivery (aOR 0.73, 95% CI 0.61-0.87). In our final multivariable model assessing neighborhood-level risk factors for severe maternal morbidity, the rate of severe maternal morbidity increased by 2.4% (95% CI 0.37-4.4%) with every 10% increase in the percentage of individuals in a Census tract who identified as Black or African American when accounting for the number of violent crimes and percentage of people identifying as White. CONCLUSION: Both individual-level and neighborhood-level risk factors were associated with severe maternal morbidity. These factors may contribute to rising severe maternal morbidity rates in the United States. Better characterization of risk factors for severe maternal morbidity is imperative for the design of clinical and public health interventions seeking to lower rates of severe maternal morbidity and maternal mortality.

Not All C-sections Are the Same: Investigating Emergency vs. Elective C-section deliveries as an Adverse Pregnancy Outcome.

Electronic Health Records (EHR) contain detailed information about a patient's medical history and can be helpful in understanding clinical outcomes among populations generally underrepresented in research, including pregnant individuals. A cesarean delivery is a clinical outcome often considered in studies as an adverse pregnancy outcome, when in reality there are circumstances in which a cesarean delivery is considered the safest or best choice given the patient's medical history, situation, and comfort. Rather than consider all cesarean deliveries to be negative outcomes, it is important to examine other risk factors that may contribute to a cesarean delivery being an adverse event. Looking at emergency admissions can be a useful way to ascertain whether or not a cesarean delivery is part of an adverse event. This study utilizes EHR data from Penn Medicine to assess patient characteristics and pregnancy-related conditions as risk factors for an emergency admission at the time of delivery. After adjusting for pregnancy number and cesarean number for each patient, preterm birth increased risk of an emergency admission, and patients younger than 25, or identifying as Black/African American, Asian, or Other/Mixed, had an increased risk. Later pregnancies and repeat cesareans decreased the risk of an emergency delivery, and White, Hispanic, and Native Hawaiian/Pacific Islander patients were at decreased risk. The same risk factors and trends were found among cesarean deliveries, except that Asian patients did not have an increased risk, and Native Hawaiian/Pacific Islander patients did not have a reduced risk in this group.

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.

OBJECTIVE: Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy and improve outcomes. MATERIALS AND METHODS: We searched English articles on EMBASE, PubMed and SCOPUS. Search terms included ML, AI, pregnancy and informatics. We included research articles and book chapters, excluding conference papers, editorials and notes. RESULTS: We identified 127 distinct studies from our queries that were relevant to our topic and included in the review. We found that supervised learning methods were more popular (n = 69) than unsupervised methods (n = 9). Popular methods included support vector machines (n = 30), artificial neural networks (n = 22), regression analysis (n = 17) and random forests (n = 16). Methods such as DL are beginning to gain traction (n = 13). Common areas within the pregnancy domain where AI and ML methods were used the most include prenatal care (e.g. fetal anomalies, placental functioning) (n = 73); perinatal care, birth and delivery (n = 20); and preterm birth (n = 13). Efforts to translate AI into clinical care include clinical decision support systems (n = 24) and mobile health applications (n = 9). CONCLUSIONS: Overall, we found that ML and AI methods are being employed to optimize pregnancy outcomes, including modern DL methods (n = 13). Future research should focus on less-studied pregnancy domain areas, including postnatal and postpartum care (n = 2). Also, more work on clinical adoption of AI methods and the ethical implications of such adoption is needed.