Spontaneous Migration of Ventriculoperitoneal Shunt Into the Right Heart and Pulmonary Vasculature.

A 73-year-old woman with ventriculoperitoneal (VP) shunt presented for stress echocardiogram for evaluation of chest pain. Transthoracic echocardiogram revealed an incidental right heart mass representing a migrated VP shunt. This case highlights the role of multimodality cardiac imaging in diagnosing right heart masses and the multidisciplinary approach to management.

Improved Margin Control of Microcystic Adnexal Carcinoma After Mohs Micrographic Surgery Compared With Wide Local Excision.

BACKGROUND: Microcystic adnexal carcinoma (MAC) is a locally aggressive and deeply infiltrative cutaneous tumor primarily treated with excision; however, there are limited data comparing outcomes by surgical approach. OBJECTIVE: To compare surgical outcomes of MAC treated with Mohs micrographic surgery (MMS) and wide local excision (WLE). METHODS: A 27-year retrospective cohort study of primary MAC was performed. Surgical (i.e. margin status after resection) and recurrence outcomes (including local recurrence [LR], nodal metastases [NM], and distance metastases [DM]) were analyzed by type of surgical approach (MMS and WLE). RESULTS: Sixty-nine MACs were included, of which 34 (49.3%) were treated with MMS and 35 (50.7%) with WLE. All MMS-treated tumors had negative margins after the first surgery attempt. Twenty-one (60.0%) tumors treated with WLE had positive margins after the first surgical attempt and required additional procedures. More tumors treated with WLE developed LR, NM, or DM, although this did not meet statistical significance. LIMITATIONS: Retrospective single institution study. CONCLUSION: Greater than half of MAC tumors treated with WLE had positive margins after the initial surgery and required multiple procedures for complete removal. Real-time complete margin assessment is important for this locally aggressive and infiltrative tumor.

Heterogeneity of Drug Allergies and Reaction Lists in Two U.S. Health Care Systems' Electronic Health Records.

BACKGROUND: Health care institutions have their own "picklist" for clinicians to document adverse drug reactions (ADRs) into the electronic health record (EHR) allergy list. Whether the lack of a nationally standardized picklist impacts clinician data entries is unknown. OBJECTIVES: The objective of this study was to assess the impact of defined reaction picklists on clinical documentation and, therefore, downstream analytics and clinical research using these data at two institutions. METHODS: ADR data were obtained from the EHRs of patients who visited the emergency department or outpatient clinics at Brigham and Women's Hospital (BWH) and University of Colorado Hospital (UCH) from 2013 to 2018. Reported drug class ADR prevalences were calculated. We investigated the reactions on each picklist and compared the top 40 reactions at each institution, as well as the top 10 reactions within each drug class. RESULTS: Of 2,160,116 patients, 640,444 (30%) had 928,973 active drug allergies. The most commonly reported drug class allergens were similar between BWH and UCH. BWH's picklist had 48 reactions, and UCH's had 160 reactions; 29 reactions were shared by both picklists. While the top four reactions overall (rash, GI upset/nausea/vomiting, hives, itching) were identical between sites, reactions by drug class exhibited greater documentation diversity. For example, while the summed prevalence of swelling-related reactions to angiotensin-converting-enzyme inhibitors was comparable across sites, swelling was represented by two terms ("swelling," "angioedema") at BWH but 11 terms at UCH (e.g., "swelling," "edema," by body locality). CONCLUSION: The availability and granularity of reaction picklists impact ADR documentation in the EHR by health care providers; picklists may partially explain variations in reported ADRs across health care systems.

Embedding, aligning and reconstructing clinical notes to explore sepsis.

OBJECTIVE: Our goal was to research and develop exploratory analysis tools for clinical notes, which now are underrepresented to limit the diversity of data insights on medically relevant applications. RESULTS: We characterize how exploratory analysis can affect representation learning on clinical narratives and present several self-developed tools to explore sepsis. Our experiments focus on patients with sepsis in the MIMIC-III Clinical Database or in our institution's research patient data repository. We found that global embeddings assist in learning local representations of clinical notes. Second, aligning at any specific time facilitates the use of learning models by pooling more available clinical notes to form a training set. Furthermore, reconstruction of the timeline enhances downstream-processing techniques by emphasizing temporal expressions and temporal relationships in clinical documentation. We demonstrate that clustering helps plot various types of clinical notes against a scale, which conveys a sense of the range or spread of the data and is useful for understanding data correlations. Appropriate exploratory analysis tools provide keen insights into preprocessing clinical notes, thereby further enhancing downstream analysis capabilities, making data driven medicine possible. Our examples can help generate better data representation of clinical documentation for models with improved performance and interpretability.

Vancomycin Hypersensitivity Reactions Documented in Electronic Health Records.

BACKGROUND: Vancomycin, the most common antimicrobial used in US hospitals, can cause diverse adverse reactions, including hypersensitivity reactions (HSRs). Yet, little is known about vancomycin reactions documented in electronic health records. OBJECTIVE: To describe vancomycin HSR epidemiology from electronic health record allergy data. METHODS: This was a cross-sectional study of patients with 1 or more encounter from 2017 to 2019 and an electronic health record vancomycin drug allergy label (DAL) in 2 US health care systems. We determined prevalence and trends of vancomycin DALs and assessed active DALs by HSR phenotype determined from structured (coded) and unstructured (free-text) data using natural language processing. We investigated demographic associations with documentation of vancomycin red man syndrome (RMS). RESULTS: Among 4,490,618 patients, 14,426 (0.3%) had a vancomycin DAL with 18,761 documented reactions (2,248 [12.0%] free-text). Quarterly mean vancomycin DALs added were 253 ± 12 and deleted were 12 ± 2. Of 18,761 vancomycin HSRs, 7,903 (42.1%) were immediate phenotypes and 3,881 (20.7%) were delayed phenotypes. Common HSRs were rash (32% of HSRs) and RMS (16% of HSRs). Anaphylaxis was coded in 6% cases of HSRs. Drug reaction eosinophilia and systemic symptoms syndrome was the most common coded vancomycin severe cutaneous adverse reaction. RMS documentation was more likely for males (odds ratio, 1.30; 95% CI, 1.17-1.44) and less likely for blacks (odds ratio, 0.59; 95% CI, 0.47-0.75). CONCLUSIONS: Vancomycin causes diverse adverse reactions, including common (eg, RMS) and severe (eg, drug reaction eosinophilia and systemic symptoms syndrome) reactions entered as DAL free-text. Anaphylaxis comprised 6% of documented vancomycin HSRs, although true vancomycin IgE-mediated reactions are exceedingly rare. Improving vancomycin DAL documentation requires more coded entry options, including a coded entry for RMS.

A dynamic reaction picklist for improving allergy reaction documentation in the electronic health record.

OBJECTIVE: Incomplete and static reaction picklists in the allergy module led to free-text and missing entries that inhibit the clinical decision support intended to prevent adverse drug reactions. We developed a novel, data-driven, "dynamic" reaction picklist to improve allergy documentation in the electronic health record (EHR). MATERIALS AND METHODS: We split 3 decades of allergy entries in the EHR of a large Massachusetts healthcare system into development and validation datasets. We consolidated duplicate allergens and those with the same ingredients or allergen groups. We created a reaction value set via expert review of a previously developed value set and then applied natural language processing to reconcile reactions from structured and free-text entries. Three association rule-mining measures were used to develop a comprehensive reaction picklist dynamically ranked by allergen. The dynamic picklist was assessed using recall at top k suggested reactions, comparing performance to the static picklist. RESULTS: The modified reaction value set contained 490 reaction concepts. Among 4 234 327 allergy entries collected, 7463 unique consolidated allergens and 469 unique reactions were identified. Of the 3 dynamic reaction picklists developed, the 1 with the optimal ranking achieved recalls of 0.632, 0.763, and 0.822 at the top 5, 10, and 15, respectively, significantly outperforming the static reaction picklist ranked by reaction frequency. CONCLUSION: The dynamic reaction picklist developed using EHR data and a statistical measure was superior to the static picklist and suggested proper reactions for allergy documentation. Further studies might evaluate the usability and impact on allergy documentation in the EHR.

Preoperative penicillin allergy testing in patients undergoing cardiac surgery.

BACKGROUND: Cefazolin is a first-line prophylactic antibiotic used to prevent surgical site infections (SSIs) in cardiac surgery. Patients with a history of penicillin allergy often receive less effective second-line antibiotics, which is associated with an increased SSI risk. OBJECTIVE: To describe the impact of preoperative penicillin allergy evaluation on perioperative cefazolin use in patients undergoing cardiac surgery. METHODS: We performed a retrospective cohort study of patients with a documented penicillin allergy who underwent cardiac surgery at the Massachusetts General Hospital from September 2015 to December 2018. We describe penicillin allergy evaluation assessment and outcomes. We evaluated the association between preoperative penicillin allergy evaluation and first-line perioperative antibiotic use using a multivariable logistic regression model. RESULTS: Of 3802 cardiac surgical patients, 510 (13%) had a documented penicillin allergy; 165 (33%) were referred to allergy and immunology practitioners. Of 160 patients (31%) who underwent penicillin allergy evaluation (ie, penicillin skin testing and, if results were negative, an amoxicillin challenge), 154 (97%) were found not to have a penicillin allergy. Patients who underwent preoperative penicillin allergy evaluation were more likely to receive the first-line perioperative antibiotic (92% vs 38%, P < .001). After adjusting for potential confounders, patients who underwent preoperative penicillin allergy evaluation had higher odds of first-line perioperative antibiotic use (adjusted odds ratio, 26.6; 95% CI, 12.8-55.2). CONCLUSION: Integrating penicillin allergy evaluation into routine preoperative care ensured that almost all evaluated patients undergoing cardiac surgery received first-line antibiotic prophylaxis, a critical component of SSI risk reduction. Further efforts are needed to increase access to preoperative allergy evaluation.

Facilitating information extraction without annotated data using unsupervised and positive-unlabeled learning.

Information extraction (IE), the distillation of specific information from unstructured data, is a core task in natural language processing. For rare entities (<1% prevalence), collection of positive examples required to train a model may require an infeasibly large sample of mostly negative ones. We combined unsupervised- with biased positive-unlabeled (PU) learning methods to: 1) facilitate positive example collection while maintaining the assumptions needed to 2) learn a binary classifier from the biased positive-unlabeled data alone. We tested the methods on a real-life use case of rare (<0.42%) entity extraction from medical malpractice documents. When tested on a manually reviewed random sample of documents, the PU model achieved an area under the precision-recall curve of0.283 and Fj of 0.410, outperforming fully supervised learning (0.022 and 0.096, respectively). The results demonstrate our method's potential to reduce the manual effort required for extracting rare entities from narrative texts.