Citations

ABSTRACTObjectivesPrior studies have raised concerns about postpartum hemorrhage (PPH) risk associated with selective serotonin and serotonin‐norepinephrine reuptake inhibitors (SSRIs and SNRIs), although limitations include possible confounding by maternal indications. The current study examined the risk of PPH associated with prenatal S/NRIs accounting for diagnosed maternal depression during pregnancy.MethodsThe current study is a secondary analysis of data from a cohort of 2213 pregnancies (1997–2010) identified from a large electronic medical record‐linkage system. Exposures were identified from electronic prescriptions records and resulted in three main groups: S/NRI‐exposed pregnancies (S/NRI users), antidepressant‐unexposed pregnancies (non‐users), and S/NRI exposure in the year prior to (but not during) pregnancy (former users). Data used to identify PPH occurrences included obstetrician chart diagnoses, diagnosis codes, and recorded estimated blood loss (EBL). The risk of PPH by exposure group was estimated using unadjusted, minimally adjusted (maternal age at delivery, delivery year, parity, and maternal smoking), and depression‐adjusted models (previous covariates and maternal depression). Supplemental analyses included an examination of PPH risk with prenatal bupropion monotherapy.ResultsThe study cohort consisted of 837 S/NRI users, 401 former users, and 786 non‐users, as well as 114 bupropion users identified for additional analyses. The frequency of PPH was 11.6% in the overall cohort, and was 14.7% for S/NRI users, 10.2% for former users, and 8.7% for non‐users. The risk of PPH was significantly higher for S/NRI users than non‐users in unadjusted (OR 1.82 [95% CI 1.33, 2.49]), minimally adjusted (aOR 1.66 [1.19, 2.31]), and depression‐adjusted models (aOR 1.46 [1.02, 2.10]). The risk of PPH was also higher for S/NRI former users than non‐users in unadjusted models (OR 1.54 [1.05, 2.26]) but not minimally adjusted (aOR 1.42 [0.95, 2.11]) or depression‐adjusted models (aOR 1.41 [0.95, 2.10]). No significant differences in PPH risk were observed between S/NRI users and former users, between bupropion users and non‐users, or between S/NRI users and bupropion users.ConclusionsPrenatal S/NRI exposure was associated with an increased risk of PPH after adjusting for maternal depression, although residual confounding and confounding by other factors (including unmeasured factors) may be possible, as the risk of PPH appeared to be similar between S/NRI users and former users. Best‐practice PPH risk management strategies should be adhered to for depressed patients, especially those who require treatment with antidepressants during pregnancy.

Objectives To examine associations between multimorbidity, the social determinants of health (SDoHs) and rheumatoid arthritis (RA) flare and remission. Methods All patients aged ≥18 years in Olmsted County, Minnesota, with incident RA in 1999–2014 were identified. Using a list of 55 chronic medical conditions, multimorbidity was defined as the presence of ≥2 conditions and substantial multimorbidity as ≥5 conditions. The Area Deprivation Index and Social Vulnerability Index (SVI) were used as proxies for adverse SDoH burden. Flare and remission were defined using Outcome Measures in Rheumatoid Arthritis Clinical Trials definitions. Mixed effects models were used to assess associations between flare/remission and multimorbidity, adverse SDoH burden and other patient characteristics. Results This study included 659 patients with incident RA. Multimorbidity and substantial multimorbidity predicted 29% (OR:1.29, 95% CI:1.04 to 1.59) and 26% (OR:1.26, 95% CI:1.03 to 1.53) higher odds of flare, respectively. Both were associated with 34% (OR:0.66, 95% CI:0.49 to 0.90) and 33% (OR:0.67, 95% CI:0.51 to 0.90) lower odds of remission, respectively. SVI predicted 8% lower odds of remission for every 0.1 increase above 0.3 (OR:0.92, 95% CI:0.85 to 0.99). Flare was also associated with female sex, smoking, younger age and shorter disease duration, but not seropositivity. Remission was also associated with male sex, never smoking, older age and longer disease duration, but not seropositivity. Conclusions Multimorbidity predicts higher odds of RA flare and lower odds of remission. Adverse SDoH burden predicts lower odds of remission. These findings have the potential to inform disease prognostication and clinician interventions.

Abstract Background and Aims Truncating variants in the TTN gene (TTNtv) are the most common genetic cause of dilated cardiomyopathy (DCM) but also occur as incidental findings in the general population. This study investigated factors associated with the clinical manifestation of TTNtv. Methods An international multicentre retrospective observational study was performed in families with TTNtv-related DCM. Shared frailty models were used to estimate associations of variant characteristics with lifetime risk of DCM, and logistic regression to estimate odds ratios (ORs) for individual-level clinical risk factor profiles (cardiac conditions, cardiovascular comorbidities, lifestyle) and DCM. Results A total of 3158 subjects in 1043 families with TTNtv-related DCM were studied. TTNtv-positive subjects were 21-fold more likely to develop DCM [OR, 21.21; 95% confidence interval (CI), 14.80–30.39]. Disease onset was earlier in males, but was similar for TTNtv of different types and locations. The presence of clinical risk factors was associated with earlier DCM onset (OR, 3.41; 95% CI, 2.06–5.64), with a prior history of atrial fibrillation having a two-fold increased odds of DCM (OR, 2.05; 95% CI, 1.27–3.32). The prevalence of clinical risk factors increased with age; however, the strength of the DCM association was greatest for young-onset (<30 years) disease (OR, 4.75; 95% CI, 2.35–9.60). Administration of beta-adrenergic receptor or renin-angiotensin system-blocking drugs prior to overt DCM was associated with 87% reduced odds of DCM (OR, .13; 95% CI, .08–.23). Conclusions Disease onset in TTNtv-associated familial DCM is dependent on individual patient context and is potentially modifiable by risk factor management and prophylactic therapeutic intervention.

Abstract Background & Aims Pregnancy can be a complex and risk-filled event for women with inflammatory bowel disease (IBD). High-quality studies in this population are lacking, with limited data on medications approved to treat IBD during pregnancy. For patients, limited knowledge surrounding pregnancy impacts pregnancy rates, medication adherence, and outcomes. Limited provider knowledge leads to highly varied practices in care affected by local dogma, available resources, individual interpretation of the literature, and fear of harming the fetus. The variations in guidelines by different societies and countries reflect this and lead to confusion for physicians and patients alike. The Global Consensus Consortium is a group of 39 IBD and content experts and 7 patient advocates from 6 continents who convened to review and assess current data and come to an agreement on best practices based on these data. Methods The GRADE (Grading of Recommendations Assessment, Development, and Evaluation) process was used when sufficient published data were available and the RAND (Research and Development) process in those instances where expert opinion was needed to guide consistent practice. Recommendations were informed by the guiding principle that maternal health best supports infant health. Results The topics were divided into ten categories with 34 GRADE recommendations and 35 consensus statements. Conclusions Overall, the goal of the group was to provide data-driven and practical guidance to improve the care of women with IBD around the globe based on the best available research.

Abstract Background To utilize high spatial resolution reconstructions for cardiac imaging at energy‐integrating detector CT (EID)‐CT with comparable noise to similar reconstructions at photon‐counting detector (PCD)‐CT, methods to control EID‐CT image noise are needed. Supervised convolutional neural networks (CNN) have shown promise for denoising, but a challenge remains to efficiently create high‐quality and unbiased estimates of noise without access to dedicated software or proprietary information, such that natural noise texture is retained in CNN‐denoised CT images. Purpose This study aims to develop and test image‐based noise estimation methods that can be used to train a CNN model, and to evaluate denoising performance and noise texture preservation for EID‐CT coronary CT angiography (cCTA) images reconstructed with high‐resolution kernels. Methods U‐net CNN models were trained for denoising. To supervise training, noise‐only images were estimated directly from high‐resolution kernel (Bv59) reconstructed EID‐CT (HR EID‐CT) patient images using two different methods: subtraction of low‐ and high‐strength iterative reconstruction (IR); subtraction of adjacent image slices with the same IR strength. The noise estimates from these methods contain differing noise texture and anatomical information. Networks were trained and validated separately for three data sets: the training data from each of the two noise‐estimation methods, and a 50%–50% partition of training data between the two methods. The trained models were applied to two sets of testing data: CT images of a uniform water phantom to measure noise power spectra (NPS), and an independent cohort of seven patient cCTA HR EID‐CT exams. The denoised patient images were compared to standard resolution EID‐CT reconstructions (Bv40). As a low‐noise reference, patient images acquired on the same day with a PCD‐CT and reconstructed using a similar kernel as HR EID‐CT were used for comparison. Results Models trained with each noise‐image estimation method denoised the HR EID‐CT images by 74%–79% to achieve a comparable noise magnitude to the HR PCD‐CT images. The peak, average, and 10% peak frequencies of the NPS of the input images (6.08, 6.24, and 12.0 cm −1 ) were better approximated by the model trained on adjacent slice subtraction (6.56, 5.87, and 11.5 cm −1 ) than by the model trained on subtraction of low‐ and high‐IR images (4.64, 5.44, and 11.3 cm −1 ). In cCTA images, the IR subtraction model images retained anatomic structures from input images but resulted in undesirable salt‐and‐pepper noise texture and CT number bias. The model trained on adjacent slice subtraction images had more natural texture and no significant bias, but the model sometimes removed small anatomic structures. The model trained on the mixed training dataset preserved both noise texture and anatomy from the model inputs and enabled visualization of small structures seen in PCD‐CT images that were previously unresolved by EID‐CT. Conclusions The noise texture and anatomical accuracy in CT images denoised with an image‐based supervised CNN are greatly influenced by the characteristics and partitioning of training data. With higher‐resolution reconstructions and noise texture‐preserving deep learning denoising, the quality of cCTA images from EID‐CT can be enhanced to enable resolvability of subtle anatomy similar to PCD‐CT.

ABSTRACT Objectives Multicenter research of geriatric emergency department (GED) care remains limited. Our objectives were to: 1. Prospectively collect data prioritized by the Geriatric Emergency care Applied Research (GEAR) network, a transdisciplinary taskforce for GED care, and create a multicenter GED research repository of prospective and electronic health record (EHR) data, 2. Assess concordance between prospective and EHR data. Methods The GEAR Standardization Study (GEARSS) is a multicenter, prospective study of older emergency department (ED) patients (65+) focusing on the 4Ms of age‐friendly care (mobility, medication safety, mentation, what matters) and elder mistreatment. Demographic and clinical data were collected via interviews by trained research assistants (RA) on Days 0, 4, 30, and 90 and linked to EHR. Prevalence of chronic comorbidities and incident delirium were measured and reported using descriptive statistics. Prospective and EHR data concordance was assessed with Cohen's Kappa. Results 999 participants were recruited from 5 EDs (3/25/2021–6/30/2022) across 3 institutions: Grady Health System, Northwestern Memorial Hospital, and Yale New Haven Health. The cohort was 57.0% female, 55.2% White, 39.1% Black, and 3.4% Hispanic, and the mean age was 75.1 years. For rheumatologic disease, peptic ulcer disease, diabetes, renal disease, and cancer, prevalence differed between prospective and EHR data by > 10%. About two‐thirds of participants were at risk for falls. Concordance between prospective and EHR data was good for ethnicity ( K = 0.73); excellent for sex ( K = 1.00), age ( K = 1.00), and race ( K = 0.98); fair for disposition ( K = 0.53); slight for ED observation status ( K = 0.33) and dementia diagnosis ( K = 0.24); poor for delirium presence ( K = 0.07). Conclusion In GEARSS, demographic variables aligned strongly between prospective and EHR data, while diagnosis, disposition, and mentation factors did not. This multicenter data source provides preliminary findings for common geriatric syndromes and conditions. Choice of measures using these data should be driven by GED research questions.

ABSTRACT Clinical and Laboratory Standards Institute (CLSI) recommends a blood culture contamination (BCC) threshold of <3%, with ≤1% considered optimal. However, there is not a standardized definition of BCC, and the effect of multiple definitions on BCC rates or what definitions laboratories use remain unknown. We surveyed 52 hospitals and analyzed 362,078 blood cultures (BCx) collected 1 September 2019 to 31 August 2021 from 62 intensive care units (ICUs) and 231 wards from 48 of these hospitals. We calculated and compared BCC rates using the College of American Pathologists (CAP) or CLSI criteria (both utilize a limited number of skin commensals to define BCC) and the comprehensive National Healthcare Safety Network (NHSN) commensal list. We characterized factors associated with BCC and related outcomes (central-line associated bloodstream infection [CLABSI] and vancomycin use). BCC, BCx positivity, and single BCx rates were monitored by 100%, 39%, and 21% of hospitals, respectively. Hospitals used CAP (65%), CLSI (17%), and NHSN (17%) criteria to define BCC. Mean BCC rate by CAP (CAP-BCC) was 1.38% for ICUs and 0.96% for wards. BCC rates remained similar by CLSI criteria but increased when using NHSN list. Sharing BCC data outside of the laboratory, measuring additional BCx quality indicators, and limiting central catheter-drawn BCx were associated with lower BCC rates. BCC was associated with higher CLABSI rates in ICUs. This study demonstrated variability in laboratory practices and opportunities to optimize BCx stewardship. IMPORTANCE Blood culture contamination (BCC) is associated with patient harm and unnecessary use of healthcare resources. BCC thresholds have been established; however, multiple BCC definitions exist. There is limited data on how BCC rates differ depending on the BCC definition used, what definitions laboratories most commonly use, or their approach to other blood cultures (BCx) quality indicators such as single rates or BCx positivity. A cross-sectional multicenter survey and analysis of BCx data from intensive care unit and wards revealed that most laboratories did not track single BCx or BCx positivity rates and that there was variability in how BCC was defined. Additionally, BCC rates were influenced by the definition used. BCC was associated with increased central-line associated bloodstream infection rates.

Objectives This study identified the prevalence of second victim experience (SVE) in a multidisciplinary sample of healthcare learners and explored personal experiences of trauma and desired forms of support following traumatic patient care events or situations. Method We used the validated SVE Support Tool-Revised and the Physician Well-Being Index (PWBI) to query healthcare learners at a large academic health system across the institution's medical, graduate medical education, and health sciences schools. The survey was open to all healthcare learners from April 8th, 2022 to May 30th, 2022. Here, we report on an analysis of respondents’ fixed responses describing the prevalence and characteristics of SVE. Results Of 2298 potential respondents, 206 answered at least one question (9.0% response rate). Of the 206 respondents, 205 answered questions inquiring if they had been a part of a stressful or traumatic patient care event or situation and 54.1% (111/205) answered in the affirmative. Of this group, 49.5% (51/103) reported feeling like a second victim (SV). The average PWBI of the exposed group was significantly higher (worse well-being) than the nonexposed group (3.4 vs 2.6, P = .0009). The most common forms of desired support for those exposed to a traumatic event and felt like an SV was conversation with peers 95.3% (41/43), family 81.4% (35/43), and a supervisor or manager 67.4% (29/43). Conclusion Findings suggest that many healthcare learners experience SVE and would benefit from connection to desired support strategies. Strategies to mitigate the impact of SVE on professional self-efficacy should be considered by education administrators. We present strategies from the literature and our institution that may be replicated for early identification and support of learners with SVE.