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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., Competing Interests: Competing interests: ALCS is an Associate Editor at Neurology. DJW provides consulting services on the topic of concussion/TBI epidemiology to the NCAA., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

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., (© 2022. The Author(s).)

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., (©Lena Davidson, Silvia P Canelón, Mary Regina Boland. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 07.06.2022.)

Objective: Postpartum depression (PPD) remains an understudied research area despite its high prevalence. The goal of this study is to develop an ontology to aid in the identification of patients with PPD and to enable future analyses with electronic health record (EHR) data., Methods: We used Protégé-OWL to construct a postpartum depression ontology (PDO) of relevant comorbidities, symptoms, treatments, and other items pertinent to the study and treatment of PPD., Results: The PDO identifies and visualizes the risk factor status of variables for PPD, including comorbidities, confounders, symptoms, and treatments. The PDO includes 734 classes, 13 object properties, and 4,844 individuals. We also linked known and potential risk factors to their respective codes in the International Classification of Diseases versions 9 and 10 that would be useful in structured EHR data analyses. The representation and usefulness of the PDO was assessed using a task-based patient case study approach, involving 10 PPD case studies. Final evaluation of the ontology yielded 86.4% coverage of PPD symptoms, treatments, and risk factors. This demonstrates strong coverage of the PDO for the PPD domain., Conclusion: The PDO will enable future researchers to study PPD using EHR data as it contains important information with regard to structured (e.g., billing codes) and unstructured data (e.g., synonyms of symptoms not coded in EHRs). The PDO is publicly available through the National Center for Biomedical Ontology (NCBO) BioPortal ( https://bioportal.bioontology.org/ontologies/PARTUMDO ) which will enable other informaticists to utilize the PDO to study PPD in other populations., Competing Interests: None declared., (Thieme. All rights reserved.)

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.

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., (© 2021. The Author(s).)

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., Competing Interests: Financial Disclosure The authors did not report any potential conflicts of interest., (Copyright © 2021 by the American College of Obstetricians and Gynecologists. Published by Wolters Kluwer Health, Inc. All rights reserved.)

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.

Objective: To develop an algorithm that infers patient delivery dates (PDDs) and delivery-specific details from Electronic Health Records (EHRs) with high accuracy; enabling pregnancy-level outcome studies in women's health., Materials and Methods: We obtained EHR data from 1,060,100 female patients treated at Penn Medicine hospitals or outpatient clinics between 2010-2017. We developed an algorithm called MADDIE: Method to Acquire Delivery Date Information from Electronic Health Records that infers a PDD for distinct deliveries based on EHR encounter dates assigned a delivery code, the frequency of code usage, and the time differential between code assignments. We validated MADDIE's PDDs against a birth log independently maintained by the Department of Obstetrics and Gynecology., Results: MADDIE identified 50,560 patients having 63,334 distinct deliveries. MADDIE was 98.6 % accurate (F 1 -score 92.1 %) when compared to the birth log. The PDD was on average 0.68 days earlier than the true delivery date for patients with only one delivery (± 1.43 days) and 0.52 days earlier for patients with more than one delivery episode (± 1.11 days)., Discussion: MADDIE is the first algorithm to successfully infer PDD information using only structured delivery codes and identify multiple deliveries per patient. MADDIE is also the first to validate the accuracy of the PDD using an external gold standard of known delivery dates as opposed to manual chart review of a sample., Conclusion: MADDIE augments the EHR with delivery-specific details extracted with high accuracy and relies only on structured EHR elements while harnessing temporal information and the frequency of code usage to identify accurate PDDs., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Menarche is the first occurrence of a woman's menstruation, an event that symbolizes reproductive capacity and the transition from childhood into womanhood. The global average age for menarche is 12 years and this has been declining in recent years. Many factors that affect the timing menarche in girls could be affected by climate change. A systematic literature review was performed regarding the timing of menarche and four publication databases were interrogated: EMBASE, SCOPUS, PubMed, and Cochrane Reviews. Themes were identified from 112 articles and related to environmental causes of perturbations in menarche (either early or late), disease causes and consequences of perturbations, and social causes and consequences. Research from climatology was incorporated to describe how climate change events, including increased hurricanes, avalanches/mudslides/landslides, and extreme weather events could alter the age of menarche by disrupting food availability or via increased toxin/pollutant release. Overall, our review revealed that these perturbations in the timing of menarche are likely to increase the disease burden for women in four key areas: mental health, fertility-related conditions, cardiovascular disease, and bone health. In summary, the climate does have the potential to impact women's health through perturbation in the timing of menarche and this, in turn, will affect women's risk of disease in future.

Enzymatic crosslinks stabilize type I collagen and are catalyzed by lysyl oxidase (LOX), a step interrupted through β-aminopropionitrile (BAPN) exposure. This study evaluated dose-dependent effects of BAPN on osteoblast gene expression of type I collagen, LOX, and genes associated with crosslink formation. The second objective was to characterize collagen produced in vitro after exposure to BAPN, and to explore changes to collagen properties under continuous cyclical substrate strain. To evaluate dose-dependent effects, osteoblasts were exposed to a range of BAPN dosages (0-10 mM) for gene expression analysis and cell proliferation. Results showed significant upregulation of BMP-1, POST, and COL1A1 and change in cell proliferation. Results also showed that while the gene encoding LOX was unaffected by BAPN treatment, other genes related to LOX activation and matrix production were upregulated. For the loading study, the combined effects of BAPN and mechanical loading were assessed. Gene expression was quantified, atomic force microscopy was used to extract elastic properties of the collagen matrix, and Fourier Transform infrared spectroscopy was used to assess collagen secondary structure for enzymatic crosslinking analysis. BAPN upregulated BMP-1 in static samples and BAPN combined with mechanical loading downregulated LOX when compared to control-static samples. Results showed a higher indentation modulus in BAPN-loaded samples compared to control-loaded samples. Loading increased the mature-to-immature crosslink ratios in control samples, and BAPN increased the height ratio in static samples. In summary, effects of BAPN (upregulation of genes involved in crosslinking, mature/immature crosslinking ratios, upward trend in collagen elasticity) were mitigated by mechanical loading.

Type I collagen morphology can be characterized using fibril D-spacing, a metric which describes the periodicity of repeating bands of gap and overlap regions of collagen molecules arranged into collagen fibrils. This fibrillar structure is stabilized by enzymatic crosslinks initiated by lysyl oxidase (LOX), a step which can be disrupted using β-aminopropionitrile (BAPN). Murine in vivo studies have confirmed effects of BAPN on collagen nanostructure and the objective of this study was to evaluate the mechanism of these effects in vitro by measuring D-spacing, evaluating the ratio of mature to immature crosslinks, and quantifying gene expression of type I collagen and LOX. Osteoblasts were cultured in complete media, and differentiated using ascorbic acid, in the presence or absence of 0.25mM BAPN-fumarate. The matrix produced was imaged using atomic force microscopy (AFM) and 2D Fast Fourier transforms were performed to extract D-spacing from individual fibrils. The experiment was repeated for quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Fourier Transform infrared spectroscopy (FTIR) analyses. The D-spacing distribution of collagen produced in the presence of BAPN was shifted toward higher D-spacing values, indicating BAPN affects the morphology of collagen produced in vitro, supporting aforementioned in vivo experiments. In contrast, no difference in gene expression was found for any target gene, suggesting LOX inhibition does not upregulate the LOX gene to compensate for the reduction in aldehyde formation, or regulate expression of genes encoding type I collagen. Finally, the mature to immature crosslink ratio decreased with BAPN treatment and was linked to a reduction in peak percent area of mature crosslink hydroxylysylpyridinoline (HP). In conclusion, in vitro treatment of osteoblasts with low levels of BAPN did not induce changes in genes encoding LOX or type I collagen, but led to an increase in collagen D-spacing as well as a decrease in mature crosslinks., Competing Interests: The authors have declared that no competing interests exist.