Faculty Bibliography

BACKGROUND:Pericardial access is complicated by two difficulties: confirming when the needle tip is in the pericardial space, and avoiding complications during access, such as inadvertently puncturing other organs. Conventional imaging tools are inadequate for addressing these difficulties, as they lack soft-tissue markers that could be used as guidance during access. A system that can both confirm access and avoid inadvertent organ injury is needed. METHODS:A 21G micropuncture needle was modified to include two small electrodes at the needle tip. With continuous bioimpedance monitoring from the electrodes, the needle was used to access the pericardium in porcine models (n  =  4). The needle was also visualized in vivo by using an electroanatomical map (n  =  2). Bioimpedance data from different tissues were analyzed retrospectively. RESULTS:Bioimpedance data collected from the subcutaneous space (992.8±13.1 Ω), anterior mediastinum (972.2±14.2 Ω), pericardial space (323.2±17.1 Ω), mid-myocardium (349.7±87.6 Ω), right ventricular cavity (235.0±9.7 Ω), lung (1142.0±172.0 Ω), liver (575.0±52.6 Ω), and blood (177.5±1.9 Ω) differed significantly by tissue type (P < .01). Phase data in the frequency domain correlated well with the needle being in the pericardial space. A simple threshold analysis effectively separated lung (threshold  =  1120.0 Ω) and blood (threshold  =  305.9 Ω) tissues from the other tissue types. CONCLUSIONS:Continuous bioimpedance monitoring from a modified micropuncture needle during pericardial access can be used to clearly differentiate tissues. Combined with traditional imaging modalities, this system allows for confirming access to the pericardial space while avoiding inadvertent puncture of other organs, creating a safer and more efficient needle-access procedure. This article is protected by copyright. All rights reserved.

There is minimal information on COVID-19 in immunocompromised individuals. We have studied 10 patients treated at 12 adult care hospitals. Ten kidney transplant recipients tested positive for SARS-CoV-2 by PCR, and 9 were admitted. The median age was 57 (IQR 47-67), 60% were male, 40% Caucasian, and 30% Black/African American. Median time from transplant to COVID-19 testing was 2822 days (IQR 1272-4592). The most common symptom was fever, followed by cough, myalgia, chills, and fatigue. The most common CXR and CT abnormality was multifocal patchy opacities. 3 patients had no abnormal findings. Leukopenia was seen in 20% of patients, and allograft function was stable in 50% of patients. 9 patients were on tacrolimus and a mycophenolic antimetabolite, and 70% were on prednisone. Hospitalized patients had their antimetabolite agent stopped. All hospitalized patients received hydroxychloroquine (HCQ) and azithromycin. 3 patients died (30%), five (50%) developed acute kidney injury. Kidney transplant recipients infected with COVID-19 should be monitored closely in the setting of lowered immunosuppression. Most individuals required hospitalization and presenting symptoms were similar to those of non-transplant individuals.

Choledochal cysts are an anatomical conundrum as they present with nonspecific symptoms generally delaying diagnosis and treatment. Its lag time remains critical, as cholangiocarcinoma, a fatal sequelae, contributes to its morbidity and mortality. Herein, we present a case of a type 1A choledochal cyst. We hope that its review on presentation, classification system, diagnosis, and management prevent complications and cataclysmic results.

BACKGROUND:Although clinical decision support (CDS) alerts are effective reminders of best practices, their effectiveness is blunted by clinicians who fail to respond to an overabundance of inappropriate alerts. An electronic health record (EHR)-integrated machine learning (ML) algorithm is a potentially powerful tool to increase the signal-to-noise ratio of CDS alerts and positively impact the clinician's interaction with these alerts in general. OBJECTIVE:This study aimed to describe the development and implementation of an ML-based signal-to-noise optimization system (SmartCDS) to increase the signal of alerts by decreasing the volume of low-value herpes zoster (shingles) vaccination alerts. METHODS:We built and deployed SmartCDS, which builds personalized user activity profiles to suppress shingles vaccination alerts unlikely to yield a clinician's interaction. We extracted all records of shingles alerts from January 2017 to March 2019 from our EHR system, including 327,737 encounters, 780 providers, and 144,438 patients. RESULTS:During the 6 weeks of pilot deployment, the SmartCDS system suppressed an average of 43.67% (15,425/35,315) potential shingles alerts (appointments) and maintained stable counts of weekly shingles vaccination orders (326.3 with system active vs 331.3 in the control group; P=.38) and weekly user-alert interactions (1118.3 with system active vs 1166.3 in the control group; P=.20). CONCLUSIONS:All key statistics remained stable while the system was turned on. Although the results are promising, the characteristics of the system can be subject to future data shifts, which require automated logging and monitoring. We demonstrated that an automated, ML-based method and data architecture to suppress alerts are feasible without detriment to overall order rates. This work is the first alert suppression ML-based model deployed in practice and serves as foundational work in encounter-level customization of alert display to maximize effectiveness.

Introduction/UNASSIGNED:Mentorship is crucial for academic success. And yet, there are few mentoring programs that address the needs of underrepresented, racially/ethnically diverse junior faculty conducting health-related research in the United States. Methods/UNASSIGNED:To expand mentoring capacity for these racially/ethnically diverse faculty, we developed a Peer Mentor Development Program (PMDP) to prepare near-peers, who have similar characteristics and personal experiences, to provide support to participants in an NIH-PRIDE funded Institute. The PMDP program is designed based on the 8-year experience of the Mentor Development Program of the NYU-Health and Hospitals Clinical Translational Science Institute. Annually, up to six alumni are selected into the PMDP, participate in the 12-hour program over 4 days, are paired with 1 to 3 scholar participants to mentor and join monthly PMDP conference calls during the ensuing year. Results/UNASSIGNED:We describe the program, participant experience and lessons learned from our first 18 peer mentors in three PMDP cohorts. Additionally, all 18 peer mentors completed a post-evaluation survey to assess the program. Overall, peers agreed that participating in the PMDP enhanced most of the specific skills targeted. Participants rated 53%-86% of skills as "more than before" participating in PMDP, demonstrating the appreciation and impact of the program. Conclusions/UNASSIGNED:The PMDP may be a model for higher education and academic medicine programs committed to mentoring and retaining racially/ethnically diverse faculty and ultimately contributing to reducing entrenched health disparities between majority and minority populations.

BACKGROUND:There is concern about the potential of an increased risk related to medications that act on the renin-angiotensin-aldosterone system in patients exposed to coronavirus disease 2019 (Covid-19), because the viral receptor is angiotensin-converting enzyme 2 (ACE2). METHODS:We assessed the relation between previous treatment with ACE inhibitors, angiotensin-receptor blockers, beta-blockers, calcium-channel blockers, or thiazide diuretics and the likelihood of a positive or negative result on Covid-19 testing as well as the likelihood of severe illness (defined as intensive care, mechanical ventilation, or death) among patients who tested positive. Using Bayesian methods, we compared outcomes in patients who had been treated with these medications and in untreated patients, overall and in those with hypertension, after propensity-score matching for receipt of each medication class. A difference of at least 10 percentage points was prespecified as a substantial difference. RESULTS:Among 12,594 patients who were tested for Covid-19, a total of 5894 (46.8%) were positive; 1002 of these patients (17.0%) had severe illness. A history of hypertension was present in 4357 patients (34.6%), among whom 2573 (59.1%) had a positive test; 634 of these patients (24.6%) had severe illness. There was no association between any single medication class and an increased likelihood of a positive test. None of the medications examined was associated with a substantial increase in the risk of severe illness among patients who tested positive. CONCLUSIONS:We found no substantial increase in the likelihood of a positive test for Covid-19 or in the risk of severe Covid-19 among patients who tested positive in association with five common classes of antihypertensive medications.