Citations

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.

Abstract Majority of hospitals still utilize manual methods for patient scheduling and predicting future appointments, resulting in longer wait times, hospital burnout and inadequate use of resources. A variety of avenues have been explored, including priority patient routing, tele-health, neural networks for improving ER efficiency, predicting no-shows, consultation duration variations, and optimizing operating room utilization. Addressing this issue, a study was conducted using 700 pre-visit notes of pancreatic patients to determine the requirement of endoscopic or biliary procedure. Through natural language processing and traditional or transfer learning algorithms, data could directly be sent to EPIC for nurses to assess in further decision making. Performance of the models was above average with the transfer learning method outperforming the traditional method. Although limited by less dataset and fewer circumstances to test the models on, the results exposed potential for future development with the possibility of patients reporting their chief concerns, in turn analyzed by algorithms, ultimately creating a smooth and effective patient itinerary.

1535 Background: Ambulatory management of select patients with febrile neutropenia (FN) may substantially decrease patients’ burden of illness and improve their quality of life, and is recommended by American Society of Clinical Oncology (ASCO) guidelines. However, in the United States, inpatient management remains the universal standard. We hereby report the health care utilization associated with the DEFeNDR remote patient monitoring (RPM) program, which leverages technology and virtual centralized nurse monitoring to facilitate ambulatory management of FN as an alternative to hospital-based care. Methods: Adult patients with cancer hospitalized with FN were assessed daily for eligibility to participate in the DEFeNDR program. Eligibility criteria included MASCC score >21 (per ASCO guidelines), and clinical judgment of the attending provider. Patients receiving cellular therapy (CAR-T, BMT) in the prior 100 days were excluded. Eligible patients agreeing to participate in the 30-day program were provided a kit containing cellular-enabled, pre-connected devices to facilitate monitoring, including twice-daily vital signs and electronic symptom questionnaires. Data were EHR-integrated in real-time and embedded decision trees facilitated alerts for adverse trends. RPM nurses responded to alerts and escalated care as indicated by pre-specified care pathways. Program assessment was performed at 20 weeks after full implementation. The primary outcome was the mean proportion of evaluable days spent inpatient (mPEDI) within 30 days of eligibility compared between those who participated in the program vs. those who declined. Results: 35 patients were offered participation in the DEFeNDR program. Of these, 17 participated and 18 declined. Baseline characteristics are provided. Participating patients experienced mPEDI of 6.9% (Standard Error [SE], 3.7%), compared with 17.7% (SE, 3.6%) for patients who declined participation. Due to small sample size, this difference (10.8%) did not reach statistical significance (Wilcoxon Rank Sums p-value, 0.15). 30-day mortality did not differ between groups. Mean inpatient days were also lower for patients participating in the program. Additionally, when readmitted, patients participating in RPM experienced a trend toward shorter length of stay and lower rates of intensive care unit utilization. Conclusions: A trend towards decreased burden of inpatient care is evident among patients participating in the DEFeNDR program. Further studies are warranted to assess comparative effectiveness of this model vs usual care.[Table: see text]

Introduction: Nitrogen dioxide (NO 2 ) is known to be a trigger for asthma exacerbation. However, little is known about the role of seasonal variation in indoor and outdoor NO 2 levels in childhood asthma in a mixed rural-urban setting of North America. Methods: This prospective cohort study, as a feasibility study, included 62 families with children (5-17 years) that had diagnosed persistent asthma residing in Olmsted County, Minnesota. Indoor and outdoor NO 2 concentrations were measured using passive air samples over 2 weeks in winter and 2 weeks in summer. We assessed seasonal variation in NO 2 levels in urban and rural residential areas and the association with asthma control status collected from participants’ asthma diaries during the study period. Results: Outdoor NO 2 levels were lower (median: 2.4 parts per billion (ppb) in summer, 3.9 ppb in winter) than the Environmental Protection Agency (EPA) annual standard (53 ppb). In winter, a higher level of outdoor NO 2 was significantly associated with urban residential living area ( P = .014) and lower socioeconomic status (SES) ( P = .027). For both seasons, indoor NO 2 was significantly higher ( P < .05) in rural versus urban areas and in homes with gas versus electric stoves ( P < .05). Asthma control status was not associated with level of indoor or outdoor NO 2 in this cohort. Conclusions: NO 2 levels were low in this mixed rural-urban community and not associated with asthma control status in this small feasibility study. Further research with a larger sample size is warranted for defining a lower threshold of NO 2 concentration with health effect on asthma in mixed rural-urban settings.

Transthyretin cardiac amyloidosis (ATTR-CA) is an increasingly recognized disease that often results in heart failure and death. Traditionally, biological staging systems are used to stratify disease severity. Reduced aerobic capacity has recently been described as useful in identifying higher risk of cardiovascular events and death. Assessment of lung volume via simple spirometry might also hold prognostic relevance. We aimed to assess the combined prognostic value of spirometry, cardiopulmonary exercise testing (CPET) and biomarker staging in ATTR-CA patients in a multi-parametric approach. We retrospectively reviewed patient records with pulmonary function and CPET testing. Patients were followed until study endpoint (MACE: composite of heart-failure-related hospitalization and all-cause death) or censure (1 April 2022). In total, 82 patients were enrolled. Median follow-up was 9 months with 31 (38%) MACE. Impaired peak VO2 and forced vital capacity (FVC) were independent predictors of MACE-free survival, with peak VO2 < 50% and FVC < 70% defining the highest risk group (HR 26, 95% CI: 5–142, mean survival: 15 months) compared to patients with the lowest risk (peak VO2 ≥ 50% and FVC ≥ 70%). Combined peak VO2, FVC and ATTR biomarker staging significantly improved MACE prediction by 35% compared to ATTR staging alone, with 67% patients reassigned a higher risk category (p < 0.01). In conclusion, combining functional and biological markers might synergistically improve risk stratification in ATTR-CA. Integrating simple, non-invasive and easily applicable CPET and spirometry in the routine management of ATTR-CA patients might prove useful for improved risk prediction, optimized monitoring and timely introduction of newer-generation therapies.

Background Birt-Hogg-Dubé (BHD) syndrome is a rare genetic syndrome caused by pathogenic or likely pathogenic germline variants in the FLCN gene. Patients with BHD syndrome have an increased risk of fibrofolliculomas, pulmonary cysts, pneumothorax and renal cell carcinoma. There is debate regarding whether colonic polyps should be added to the criteria. Previous risk estimates have mostly been based on small clinical case series. Methods A comprehensive review was conducted to identify studies that had recruited families carrying pathogenic or likely pathogenic variants in FLCN . Pedigree data were requested from these studies and pooled. Segregation analysis was used to estimate the cumulative risk of each manifestation for carriers of FLCN pathogenic variants. Results Our final dataset contained 204 families that were informative for at least one manifestation of BHD (67 families informative for skin manifestations, 63 for lung, 88 for renal carcinoma and 29 for polyps). By age 70 years, male carriers of the FLCN variant have an estimated 19% (95% CI 12% to 31%) risk of renal tumours, 87% (95% CI 80% to 92%) of lung involvement and 87% (95% CI 78% to 93%) of skin lesions, while female carriers had an estimated 21% (95% CI 13% to 32%) risk of renal tumours, 82% (95% CI 73% to 88%) of lung involvement and 78% (95% CI 67% to 85%) of skin lesions. The cumulative risk of colonic polyps by age 70 years old was 21% (95% CI 8% to 45%) for male carriers and 32% (95% CI 16% to 53%) for female carriers. Conclusions These updated penetrance estimates, based on a large number of families, are important for the genetic counselling and clinical management of BHD syndrome.