Citations

Abstract Purpose To develop a pharmacist-driven, exploratory pharmacogenomics implementation model with the goal of creating a process for pharmacists to interpret pharmacogenomics results from RIGHT 10K Study samples and provide electronic consults to providers. Summary A train-the-trainer model program was initiated whereby pharmacogenomics pharmacists developed a documentation template and a quick reference guide as a standard guide to train other pharmacists. Pharmacists completed electronic consults (e-consults) reviewing pharmacogenomics results, with reference to drug-gene interactions, for patients with “semi-urgent” and “clinically actionable” results, defined as those indicating a potential for gene-drug interactions to cause major harm and those indicating a potential for an adverse drug reaction or reduced efficacy, respectively. Outcomes measured included the number of consults over time, number and role of pharmacists involved, average time to complete e-consults over time, and gene-drug pairs for semi-urgent consults per month. A total of 395 pharmacists were trained. The total number of e-consults completed was 2,843: 61 semi-urgent and 2,782 clinically actionable consults. The average time spent per consult was 24 minutes, and the average number of e-consults per pharmacist was 7. CYP2C19-clopidogrel was the most common gene-drug pair targeted in semi-urgent consults. Conclusion Pharmacy leaders planning to implement similar pharmacogenomics programs can utilize this data to estimate hiring needs for future pharmacogenomics implementation, while also considering the potential additional cost of developing resources.

Background Cold snare endoscopic mucosal resection (CS-EMR) can reduce the risks associated with electrocautery during colon polyp resection. Data on efficacy are variable. This systematic review and meta-analysis aimed to estimate the pooled efficacy and safety rates of CS-EMR. Methods We conducted a comprehensive literature search of multiple databases, from inception to March 2023, for studies addressing outcomes of CS-EMR for colon polyps. The weighted pooled estimates with 95 %CIs were calculated using the random effects model. I2 statistics were used to evaluate heterogeneity. Results 4137 articles were reviewed, and 16 studies, including 2592 polyps in 1922 patients (51.4 % female), were included. Overall, 54.4 % of polyps were adenomas, 45 % were sessile serrated lesions (SSLs), and 0.6 % were invasive carcinomas. Polyp recurrence after CS-EMR was 6.7 % (95 %CI 2.4 %–17.4 %, I2 = 94 %). The recurrence rate was 12.3 % (95 %CI 3.4 %–35.7 %, I2 = 94 %) for polyps ≥ 20 mm, 17.1 % (95 %CI 4.6 %–46.7 %, I2 = 93 %) for adenomas, and 5.7 % (95 %CI 3.2 %–9.9 %, I2 = 50 %) for SSLs. The pooled intraprocedural bleeding rate was 2.6 % (95 %CI 1.5 %–4.5 %, I2 = 51 %), the delayed bleeding rate was 1.5 % (95 %CI 0.8 %–2.7 %, I2 = 18 %), and no perforations or post-polypectomy syndromes were reported, with estimated rates of 0.6 % (95 %CI 0.3 %–1.3 %, I2 = 0 %) and 0.6 % (95 %CI 0.3 %–1.4 %, I2 = 0 %), respectively. Conclusion CS-EMR demonstrated an excellent safety profile for colon polyps, with variable recurrence rates based on polyp size and histology. Large prospective studies are needed to validate these findings.

Abstract Despite the high incidence of COVID-19 worldwide, clinical experience with severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) in inborn errors of immunity remains limited. Recent studies have shown that patients with defects in type 1 interferon (IFN)-related pathways or those with autoantibodies against type 1 IFNs develop severe COVID-19. We reported the clinical course of 22 patients with CTLA-4 insufficiency and COVID-19 and retrospectively examined autoantibodies against type 1 IFNs at baseline. Data were obtained from the patient interviews and chart reviews. Screening for anti-IFN autoantibodies was performed using a multiplex particle-based assay. Student t test, Mann Whitney, analysis of variance, or χ2 tests were used where appropriate. Twenty-two patients aged from 8 months to 54 years, with genetically confirmed CLTA-4 insufficiency, developed COVID-19 from 2020 to 2022. The most common symptoms were fever, cough, and nasal congestion, and the median duration of illness was 7.5 days. Twenty patients (91%) developed mild COVID-19 and were treated as outpatients. Two patients were hospitalized because of COVID-19 pneumonia but did not require mechanical ventilation. Ten (45%) patients were vaccinated at the time of their first COVID-19 infection. Eleven patients received outpatient treatment with monoclonal antibodies against the SARS-CoV-2 spike protein. During the study period, 17 patients were vaccinated against SARS-CoV-2, with no severe vaccine-related adverse effects. Although median anti-S titers following vaccination or infection were lower in patients receiving immunoglobulin replacement therapy (IGRT) (349 IU/dL) than in those not receiving IGRT (2594 IU/dL; P = .15); 3 of 9 patients on IGRT developed titers >2000 IU/dL. All patients tested negative for autoantibodies against IFN-α, IFN-β, and IFN-ω at baseline. Most patients with CTLA-4 insufficiency and COVID-19 had nonsevere disease, lacked autoantibodies against type 1 IFNs, and tolerated messenger RNA vaccines with few adverse effects. Whether our findings can be extrapolated to patients receiving CTLA-4-targeting checkpoint inhibitors requires further studies.

The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.

Background: Latissimus dorsi transfer (LDT) has been purported to restore motion in patients undergoing reverse shoulder arthroplasty (RSA) who have preoperative combined loss of forward elevation (FE) and external rotation (ER). This systematic review summarizes the available evidence for the functional outcomes and complications after RSA with LDT. Furthermore, the effect of implant design and whether a concomitant teres major transfer (TMT) was performed were studied. Methods: A systematic review was performed per Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We queried PubMed/MEDLINE, Embase, Web of Science, and Cochrane databases to identify articles reporting on LDT with RSA to restore ER. Our primary outcomes were ER, FE, Constant score, and complication incidence. Secondarily, we reported postoperative internal rotation (IR) and compared ER, FE, and Constant score based on lateralized versus medialized global implant design and whether concomitant TMT was performed. Results: Nineteen studies were evaluated; functional outcomes were assessed in 16 articles reporting on 258 RSAs (123 LDT, 135 LDT-TMT). Surgical indication was most commonly cuff tear arthropathy and massive irreparable cuff tear. Mean ER was −12° preoperatively and 25° postoperatively, FE was 72° preoperatively and 141° postoperatively. Mean postoperative Constant score was 65. Of 138 patients (8 studies) describing IR, only 25% reported a mean postoperative IR ≥L3. Subanalysis comparing lateralized versus medialized implants and whether TMT was concomitantly performed demonstrated no significant difference in postoperative ER, FE, and Constant score, nor preoperative to postoperative improvement in ER and FE. The complication rate was 14.1% (of 291 shoulders from 16 studies), including tear in the tendon transfer (n = 3), revision tendon repair (n = 1), nerve-related complication (n = 9), and dislocation (n = 9). Conclusions: RSA with LDT is a reliable option to restore motion, with a comparable complication rate with standard RSA. The use of medialized versus lateralized implants and whether the TM was concomitantly transferred may not influence clinical outcomes. Level of Evidence: Level IV . See Instructions for Authors for a complete description of levels of evidence.

Introduction: Acrocyanosis and erythema pernio are 2 dermatologic manifestations of vasospastic changes. Primary care providers should consider that these conditions can occur as primary or idiopathic conditions and as secondary conditions related to another disease or medication. Herein we describe a case of acrocyanosis and erythema pernio attributed to vincristine therapy. Case Description: A 22-year-old man was evaluated for discomfort and red lesions involving the toes of both feet for several weeks. He had completed chemotherapy 1 month earlier for Ewing sarcoma in the right femur. Local control for the primary tumor included wide local excision and reconstruction with a vascularized fibular allograft from the right fibula. On examination, his right foot was dark blue and cool. Toes on both feet had nonpainful erythematous papules. After the case was discussed with the patient’s oncology team, the diagnosis was medication-induced acrocyanosis of the right foot and bilateral erythema pernio. Treatment consisted of supportive care to keep the feet warm and promote circulation to the feet. At 2-week follow-up, the patient’s symptoms and the appearance of his feet had markedly improved. Discussion: Primary care clinicians should be able to recognize dermatologic manifestations of vasospastic changes, including acrocyanosis and erythema pernio, and rule out possible secondary causes, such as pharmacologic agents. This patient’s history of therapy for Ewing sarcoma prompted consideration of medication-induced vasospastic changes most likely related to the adverse vasospastic effects of vincristine. Symptoms should improve with cessation of the offending medication.

Respiratory disorders, being one of the leading causes of disability worldwide, account for constant evolution in management technologies, resulting in the incorporation of artificial intelligence (AI) in the recording and analysis of lung sounds to aid diagnosis in clinical pulmonology practice. Although lung sound auscultation is a common clinical practice, its use in diagnosis is limited due to its high variability and subjectivity. We review the origin of lung sounds, various auscultation and processing methods over the years and their clinical applications to understand the potential for a lung sound auscultation and analysis device. Respiratory sounds result from the intra-pulmonary collision of molecules contained in the air, leading to turbulent flow and subsequent sound production. These sounds have been recorded via an electronic stethoscope and analyzed using back-propagation neural networks, wavelet transform models, Gaussian mixture models and recently with machine learning and deep learning models with possible use in asthma, COVID-19, asbestosis and interstitial lung disease. The purpose of this review was to summarize lung sound physiology, recording technologies and diagnostics methods using AI for digital pulmonology practice. Future research and development in recording and analyzing respiratory sounds in real time could revolutionize clinical practice for both the patients and the healthcare personnel.

Abstract Bowel sounds have been previously used to study intestinal motility and overall digestive health in various clinical settings. However, the blurred definition of bowel sounds and their subtypes, limited resources for interpretation, poor sensitivity, and low positive predictive value led to their restricted utility. Recent advances in artificial intelligence and machine learning have steered interest in developing unique tools using the phonoenterogram to analyze diverse bowel sounds. In our study, bowel sounds were recorded from eight healthy volunteers using the Eko Duo stethoscope. A novel deep-learning algorithm was designed to classify the recordings into baseline or prominent bowel sounds. A total of 11,210 data points (5,605 balanced sounds) were used to train and test the model to yield an accuracy of 0.895, a precision of 0.890, and a recall of 0.854 reflecting successful segregation of these sounds into respective groups. More extensive studies enrolling healthy and diseased subjects with a device specifically tailored to record bowel sounds are needed to generalize these results and determine their application in the patient population.

Abstract Blood pressure measurement in current medical practice relies on manual methods with the most widely used modality being sphygmomanometers. Utilizing the principle of Photoplethysmography, it is possible to provide an accurate reading of one’s blood pressure through light signals and photodetector devices. This research paper introduces a new Artificial Intelligence driven approach to predict Blood pressure levels and classify them according to the updated ACC (American College of Cardiology) criteria as Normal, Elevated, Stage I, and II Hypertension from the given PPG signal values using Machine Learning Models. This research paper aims to accurately read the Systolic and Diastolic Blood Pressure using Artificial Intelligence, place them into the correct value bins and further prove that the blood pressure values differ based on different skin tones in different light wavelengths such as red, infrared, and green. Machine Learning models such as the Support Vector Machine have shown an accuracy of 70.58% for Systolic Blood Pressure and Decision Tree with an accuracy of 74.4% for Diastolic Blood Pressure classification. The models used in this research are Support Vector Machine, Decision Tree and K-Nearest Neighbor. This research study has future applications and extensions to predict blood pressure levels for patients with different skin tones under different light radiations and PPG signal readings. Neural Network models will be developed to compare the blood predictions from this work.