Banerjee, Imon

Aims Increased LV wall thickness is frequently encountered in transthoracic echocardiography (TTE). While accurate and early diagnosis is clinically important, given the differences in available therapeutic options and prognosis, an extensive workup is often required to establish the diagnosis. We propose the first echo-based, automated deep learning model with a fusion architecture to facilitate the evaluation and diagnosis of increased left ventricular (LV) wall thickness. Methods and Results Patients with an established diagnosis of increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 to 11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into 80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE views were used to optimize a pre-trained InceptionResnetV2 model. Each model output was used to train a meta-learner under a fusion architecture. Model performance was assessed by multiclass area under the receiver operating characteristic curve (AUROC). A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191 HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual view-dependent models, the apical 4 chamber model had the best performance (AUROC: HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the view-dependent models (AUROC: CA: 0.90, HCM: 0.93, and HTN/other: 0.92). Conclusion The echo-based InceptionResnetV2 fusion model can accurately classify the main etiologies of increased LV wall thickness and can facilitate the process of diagnosis and workup.

Abstract Objective We sought to ascertain perceived factors affecting women’s career development efforts in the American Medical Informatics Association (AMIA) and to provide recommendations for improvements. Materials and Methods Data were collected using a 27-item survey administered via the AMIA newsletter and other social channels. Survey questions comprised 3 demographics, 15 Likert-scale, and 9 open-ended items. Likert-scale responses were summarized across respondent ages, career stages, and career domains, and open-ended responses were thematically analyzed. Results We received survey responses from 109 AMIA women members. Our findings demonstrate that AMIA had made strides in promoting career development, and the most effective AMIA efforts included social events (83%), panel discussions (80%), and scientific sessions (79%). However, despite these efforts, women members perceived that gender-specific challenges persisted within AMIA, and recognized the need for increased networking opportunities (96%), raising awareness of gender-specific challenges (95%), and encouraging gender proportional representation in leadership (92%). Discussion International and national biomedical informatics professional communities have put forth efforts to address gender-specific issues in career development. Yet, our study identified that some of these, including the deep-rooted gender power hierarchy and bias, are still perceived as profound in AMIA. Conclusion Even though existing career development efforts for women are highly effective, important perceived gender-specific career development issues require further attention and investigation to improve existing AMIA activities.

Abstract Background Easy identification of immunocompromised hosts (ICHs) would allow for stratification of culture results based on host type. Methods We utilized antimicrobial stewardship program (ASP) team notes written during handshake stewardship rounds in the pediatric intensive care unit (PICU) as the gold standard for host status; clinical notes from the primary team, medication orders during the encounter, problem list, and billing diagnoses documented prior to the ASP documentation were extracted to develop models that predict host status. We calculated performance for three models based on diagnoses/medications, with and without natural language processing from clinical notes. The susceptibility of pathogens causing bacteremia to commonly used empiric antibiotic regimens was then stratified by host status. Results We identified 844 antimicrobial episodes from 666 unique patients; 160 (18.9%) were identified as ICHs. We randomly selected 675 initiations (80%) for model training and 169 initiations (20%) for testing. A rule-based model using diagnoses and medications alone yielded a sensitivity of 0.87 (08.6–0.88), specificity of 0.93 (0.92–0.93), and positive predictive value (PPV) of 0.74 (0.73–0.75). Adding clinical notes into XGBoost model led to improved specificity of 0.98 (0.98–0.98) and PPV of 0.9 (0.88–0.91), but with decreased sensitivity 0.77 (0.76–0.79). There were 77 bacteremia episodes during the study period identified and a host-specific visualization was created. Conclusions An electronic health record–based phenotype based on notes, diagnoses, and medications identifies ICH in the PICU with high specificity.