Sharpe, Emily E.

Background Patients with self-reported antibiotic allergies have a higher cost of care, more frequent infections with resistant bacteria and worse health outcomes than patients without antibiotic allergies. Ultimately, less than 5% of patients who report a penicillin allergy have a clinically significant immune-mediated hypersensitivity reaction when tested. As 10%–30% of the population of pregnant patients are colonised for group B Streptococcus (GBS) and guidelines recommend penicillin as the treatment of choice for GBS, current recommendations support penicillin allergy testing in pregnant patients who report an allergy. Methods and intervention In this quality improvement project, nursing staff used an algorithm outlining inclusion and exclusion criteria to determine which patients were eligible to have penicillin allergy testing completed. Penicillin allergy testing consisted of a skin test using benzylpenicilloyl polylysine (Pre-Pen), penicillin G potassium, amoxicillin and alkaline hydrolysis mix (penicilloate) as a prick skin test, followed by intradermal skin test and finally an oral challenge with either amoxicillin or penicillin. Patient outcomes were analysed to evaluate the impact of the intervention. Results Of the 1266 patients receiving prenatal care during the intervention, 236 (19%) reported a history of penicillin allergy, and 212 if these were eligible for testing. 150 of the eligible patients were offered penicillin allergy testing. 101 patients (67%) completed testing and 49 (33%) declined testing. Seven patients (7%) had positive penicillin allergy testing, while 94 patients (93%) had negative penicillin allergy testing and were immediately de-labelled as penicillin allergic. Seventeen of the de-labelled patients subsequently tested positive for GBS colonisation, and all received intrapartum penicillin without adverse events. Conclusions Pursuing penicillin allergy testing for pregnant patients with reported penicillin allergy is a safe and feasible approach, allowing for allergy de-labelling and safe, guideline-driven antimicrobial therapy during subsequent labour and delivery hospitalisations. Cost-effectiveness of the allergy testing and impact on later episodes of care should be further investigated.

Abstract Background Cardiovascular disease (CVD) has been identified as a major threat to maternal health in the US and UK with cardiomyopathy being one of the most common acquired CVD in the pregnant and postpartum period. Diagnosing cardiomyopathy in pregnancy is challenging due to an overlap of cardiovascular symptoms with normal pregnancy symptoms. Purpose The purpose of this study was to evaluate the effectiveness of an ECG based deep learning model in identifying cardiomyopathy among pregnant and postpartum women. Methods We utilized an ECG based deep learning model to detect cardiomyopathy in a cohort of pregnant or postpartum women seen at multiple hospital sites. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. Results 1,807 women were included. 7%, 10% and 13% had LVEF ≤35%, <45% and <50% respectively. The ECG based deep learning model identified cardiomyopathy with an AUC of 0.92 for left ventricular ejection fraction (LVEF) ≤35%, 0.89 for LVEF <45% and 0.87 for LVEF <50%. For LVEF ≤35%, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to white (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 and 0.72 respectively. Conclusions A deep learning model effectively identifies cardiomyopathy in pregnant or postpartum women, outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): This study was made possible using resources supported by the Mayo Clinic Women's Health Research Center and the Mayo Clinic Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program funded by the National Institutes of Health (NIH), grant number K12 HD065987. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Background. Dexmedetomidine is a selective α-2 agonist commonly used for sedation that has been used in obstetric anesthesia for multimodal labor analgesia, postcesarean delivery analgesia, and perioperative shivering. This study evaluated the role of intravenous dexmedetomidine to provide rescue analgesia and/or sedation during cesarean delivery under neuraxial anesthesia. Methods. We conducted a single-center, retrospective cohort study of all parturients undergoing cesarean delivery under neuraxial anesthesia between December 1, 2018, and November 30, 2019, who required supplemental analgesia during the procedure. Patients were divided into two groups: patients who received intravenous dexmedetomidine (Dexmed group) and patients who received adjunct medications such as fentanyl, midazolam, ketamine, and nitrous oxide (Standard group). Primary outcome was incidence of conversion to general anesthesia. Results. During the study period, 107 patients received adjunct medications. There was no difference in conversion to general anesthesia between the Dexmed group and the Standard group (6% (4/62) vs. 9% (4/45); p = 0.718 ). In the Dexmed group, the mean dexmedetomidine dose received was 37 μg (range 10 to 140 μg). While the use of inotropic/vasopressor medications was common and similar in both groups, there was an increase in the incidence of bradycardia (Dexmed 15% vs. Standard 2%; p = 0.042 ) but not hypotension (Dexmed 24% vs. Standard 24%; p = 1.00 ) in the Dexmed group. Conclusion. In patients who required supplemental analgesia for cesarean delivery, those who received dexmedetomidine versus other medications had a similar rate of conversion to general anesthesia, a statistically significant increase in bradycardia, but no difference in the incidence of hypotension.

Abstract Background Cardiovascular disease (CVD) has been identified as a major threat to maternal health in the US and UK with cardiomyopathy being one of the most common acquired CVD in the pregnant and postpartum period. Diagnosing cardiomyopathy in pregnancy is challenging due to an overlap of cardiovascular symptoms with normal pregnancy symptoms. Purpose The purpose of this study was to evaluate the effectiveness of an ECG based deep learning model in identifying cardiomyopathy among pregnant and postpartum women. Methods We utilized an ECG based deep learning model to detect cardiomyopathy in a cohort of pregnant or postpartum women seen at multiple hospital sites. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. Results 1,807 women were included. 7%, 10% and 13% had LVEF ≤35%, <45% and <50% respectively. The ECG based deep learning model identified cardiomyopathy with an AUC of 0.92 for left ventricular ejection fraction (LVEF) ≤35%, 0.89 for LVEF <45% and 0.87 for LVEF <50%. For LVEF ≤35%, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to white (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 and 0.72 respectively. Conclusions A deep learning model effectively identifies cardiomyopathy in pregnant or postpartum women, outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): This study was made possible using resources supported by the Mayo Clinic Women's Health Research Center and the Mayo Clinic Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program funded by the National Institutes of Health (NIH), grant number K12 HD065987. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Abstract Aims Cardiovascular disease is a major threat to maternal health, with cardiomyopathy being among the most common acquired cardiovascular diseases during pregnancy and the postpartum period. The aim of our study was to evaluate the effectiveness of an electrocardiogram (ECG)-based deep learning model in identifying cardiomyopathy during pregnancy and the postpartum period. Methods and results We used an ECG-based deep learning model to detect cardiomyopathy in a cohort of women who were pregnant or in the postpartum period seen at Mayo Clinic. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. The study cohort included 1807 women; 7%, 10%, and 13% had left ventricular ejection fraction (LVEF) of 35% or less, <45%, and <50%, respectively. The ECG-based deep learning model identified cardiomyopathy with AUCs of 0.92 (LVEF ≤ 35%), 0.89 (LVEF < 45%), and 0.87 (LVEF < 50%). For LVEF of 35% or less, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to White (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 to 0.86 and 0.72, respectively. Conclusions An ECG-based deep learning model effectively identifies cardiomyopathy during pregnancy and the postpartum period and outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting.

Antisynthetase syndrome is a rare multisystem autoimmune disorder which clinically manifests with myositis, arthritis, interstitial lung disease, Raynaud phenomenon, and skin hyperkeratosis. Lung involvement represents the most severe form of disease and has rarely been reported in pregnancy. We present the case of a 22-year-old woman with antisynthetase syndrome and severe restrictive pulmonary disease who experienced a successful pregnancy and delivery. We discuss anesthetic considerations and highlight the importance of a multidisciplinary team approach in caring for parturients with multifactorial medical conditions.