Browsing by Author "Bedoya, Armando"
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Item Open Access An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection(2017-08-19) Futoma, Joseph; Hariharan, Sanjay; Sendak, Mark; Brajer, Nathan; Clement, Meredith; Bedoya, Armando; O'Brien, Cara; Heller, KatherineSepsis is a poorly understood and potentially life-threatening complication that can occur as a result of infection. Early detection and treatment improves patient outcomes, and as such it poses an important challenge in medicine. In this work, we develop a flexible classifier that leverages streaming lab results, vitals, and medications to predict sepsis before it occurs. We model patient clinical time series with multi-output Gaussian processes, maintaining uncertainty about the physiological state of a patient while also imputing missing values. The mean function takes into account the effects of medications administered on the trajectories of the physiological variables. Latent function values from the Gaussian process are then fed into a deep recurrent neural network to classify patient encounters as septic or not, and the overall model is trained end-to-end using back-propagation. We train and validate our model on a large dataset of 18 months of heterogeneous inpatient stays from the Duke University Health System, and develop a new "real-time" validation scheme for simulating the performance of our model as it will actually be used. Our proposed method substantially outperforms clinical baselines, and improves on a previous related model for detecting sepsis. Our model's predictions will be displayed in a real-time analytics dashboard to be used by a sepsis rapid response team to help detect and improve treatment of sepsis.Item Open Access Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?(Frontiers in physiology, 2021-01) Giri, Paresh C; Chowdhury, Anand M; Bedoya, Armando; Chen, Hengji; Lee, Hyun Suk; Lee, Patty; Henriquez, Craig; MacIntyre, Neil R; Huang, Yuh-Chin TAnalysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes, and carbon monoxide diffusion capacity of the lung (DLCO). The results are usually interpreted by the clinicians using discrete numeric data according to published guidelines. PFT interpretations by clinicians, however, are known to have inter-rater variability and the inaccuracy can impact patient care. This variability may be caused by unfamiliarity of the guidelines, lack of training, inadequate understanding of lung physiology, or simply mental lapses. A rules-based automated interpretation system can recapitulate expert's pattern recognition capability and decrease errors. ML can also be used to analyze continuous data or the graphics, including the flow-volume loop, the DLCO and the nitrogen washout curves. These analyses can discover novel physiological biomarkers. In the era of wearables and telehealth, particularly with the COVID-19 pandemic restricting PFTs to be done in the clinical laboratories, ML can also be used to combine mobile spirometry results with an individual's clinical profile to deliver precision medicine. There are, however, hurdles in the development and commercialization of the ML-assisted PFT interpretation programs, including the need for high quality representative data, the existence of different formats for data acquisition and sharing in PFT software by different vendors, and the need for collaboration amongst clinicians, biomedical engineers, and information technologists. Hurdles notwithstanding, the new developments would represent significant advances that could be the future of PFT, the oldest test still in use in clinical medicine.Item Open Access Evaluation of ML-Based Clinical Decision Support Tool to Replace an Existing Tool in an Academic Health System: Lessons Learned.(Journal of personalized medicine, 2020-08-27) Woo, Myung; Alhanti, Brooke; Lusk, Sam; Dunston, Felicia; Blackwelder, Stephen; Lytle, Kay S; Goldstein, Benjamin A; Bedoya, ArmandoThere is increasing application of machine learning tools to problems in healthcare, with an ultimate goal to improve patient safety and health outcomes. When applied appropriately, machine learning tools can augment clinical care provided to patients. However, even if a model has impressive performance characteristics, prospectively evaluating and effectively implementing models into clinical care remains difficult. The primary objective of this paper is to recount our experiences and challenges in comparing a novel machine learning-based clinical decision support tool to legacy, non-machine learning tools addressing potential safety events in the hospitals and to summarize the obstacles which prevented evaluation of clinical efficacy of tools prior to widespread institutional use. We collected and compared safety events data, specifically patient falls and pressure injuries, between the standard of care approach and machine learning (ML)-based clinical decision support (CDS). Our assessment was limited to performance of the model rather than the workflow due to challenges in directly comparing both approaches. We did note a modest improvement in falls with ML-based CDS; however, it was not possible to determine that overall improvement was due to model characteristics.Item Open Access Interstitial lung disease in a veterans affairs regional network; a retrospective cohort study.(PloS one, 2021-01) Bedoya, Armando; Pleasants, Roy A; Boggan, Joel C; Seaman, Danielle; Reihman, Anne; Howard, Lauren; Kundich, Robert; Welty-Wolf, Karen; Tighe, Robert MBackground
The epidemiology of Interstitial Lung Diseases (ILD) in the Veterans Health Administration (VHA) is presently unknown.Research question
Describe the incidence/prevalence, clinical characteristics, and outcomes of ILD patients within the Veteran's Administration Mid-Atlantic Health Care Network (VISN6).Study design and methods
A multi-center retrospective cohort study was performed of veterans receiving hospital or outpatient ILD care from January 1, 2008 to December 31st, 2015 in six VISN6 facilities. Patients were identified by at least one visit encounter with a 515, 516, or other ILD ICD-9 code. Demographic and clinical characteristics were summarized using median, 25th and 75th percentile for continuous variables and count/percentage for categorical variables. Characteristics and incidence/prevalence rates were summarized, and stratified by ILD ICD-9 code. Kaplan Meier curves were generated to define overall survival.Results
3293 subjects met the inclusion criteria. 879 subjects (26%) had no evidence of ILD following manual medical record review. Overall estimated prevalence in verified ILD subjects was 256 per 100,000 people with a mean incidence across the years of 70 per 100,000 person-years (0.07%). The prevalence and mean incidence when focusing on people with an ILD diagnostic code who had a HRCT scan or a bronchoscopic or surgical lung biopsy was 237 per 100,000 people (0.237%) and 63 per 100,000 person-years respectively (0.063%). The median survival was 76.9 months for 515 codes, 103.4 months for 516 codes, and 83.6 months for 516.31.Interpretation
This retrospective cohort study defines high ILD incidence/prevalence within the VA. Therefore, ILD is an important VA health concern.Item Open Access Overcoming barriers to the adoption and implementation of predictive modeling and machine learning in clinical care: what can we learn from US academic medical centers?(JAMIA open, 2020-07) Watson, Joshua; Hutyra, Carolyn A; Clancy, Shayna M; Chandiramani, Anisha; Bedoya, Armando; Ilangovan, Kumar; Nderitu, Nancy; Poon, Eric GThere is little known about how academic medical centers (AMCs) in the US develop, implement, and maintain predictive modeling and machine learning (PM and ML) models. We conducted semi-structured interviews with leaders from AMCs to assess their use of PM and ML in clinical care, understand associated challenges, and determine recommended best practices. Each transcribed interview was iteratively coded and reconciled by a minimum of 2 investigators to identify key barriers to and facilitators of PM and ML adoption and implementation in clinical care. Interviews were conducted with 33 individuals from 19 AMCs nationally. AMCs varied greatly in the use of PM and ML within clinical care, from some just beginning to explore their utility to others with multiple models integrated into clinical care. Informants identified 5 key barriers to the adoption and implementation of PM and ML in clinical care: (1) culture and personnel, (2) clinical utility of the PM and ML tool, (3) financing, (4) technology, and (5) data. Recommendation to the informatics community to overcome these barriers included: (1) development of robust evaluation methodologies, (2) partnership with vendors, and (3) development and dissemination of best practices. For institutions developing clinical PM and ML applications, they are advised to: (1) develop appropriate governance, (2) strengthen data access, integrity, and provenance, and (3) adhere to the 5 rights of clinical decision support. This article highlights key challenges of implementing PM and ML in clinical care at AMCs and suggests best practices for development, implementation, and maintenance at these institutions.Item Open Access Performance of the National Early Warning Score in Hospitalized Patients With Kidney Failure on Maintenance Hemodialysis.(Kidney medicine, 2022-08) Cavalier, Joanna; Zhao, Congwen; Scialla, Julia; Bedoya, Armando; Goldstein, Benjamin AItem Open Access Systemic Bevacizumab for Recurrent Respiratory Papillomatosis: A Single Center Experience of Two Cases.(The American journal of case reports, 2017-07-31) Bedoya, Armando; Glisinski, Kristen; Clarke, Jeffrey; Lind, Richard N; Buckley, Charles Edward; Shofer, ScottBACKGROUND Recurrent respiratory papillomatosis (RRP), caused by human papillomavirus (HPV), is the most common benign neoplasm of the larynx and central airways. RRP has a significant impact on quality life and high annual costs to healthcare. Currently, there is no cure for RRP, leading to repeated debulking operations for symptomatic palliation. Various local adjuvant therapies have also been studied with mixed efficacy. HPV oncogene products increase expression of vascular endothelial growth factor (VEGF) providing a potential target for treatment of RRP. Bevacizumab, a recombinant monoclonal antibody that inhibits VEGF, has shown efficacy in patients with localized disease. CASE REPORT We present two cases of extensive airway and parenchymal RRP successfully managed with systemically administered bevacizumab, a recombinant monoclonal antibody that inhibits VEGF. CONCLUSIONS Bevacizumab has shown efficacy in patients with localized disease, but here we illustrate the potential of bevacizumab for patients with extensive parenchymal burden as well as provide a brief review of the literature.Item Open Access Vitals are Vital: Simpler Clinical Data Model Predicts Decompensation in COVID-19 Patients(ACI Open, 2022-01) Cavalier, Joanna Schneider; O'Brien, Cara L; Goldstein, Benjamin A; Zhao, Congwen; Bedoya, ArmandoAbstract Objective Several risk scores have been developed and tested on coronavirus disease 2019 (COVID-19) patients to predict clinical decompensation. We aimed to compare an institutional, automated, custom-built early warning score (EWS) to the National Early Warning Score (NEWS) in COVID-19 patients. Methods A retrospective cohort analysis was performed on patients with COVID-19 infection who were admitted to an intermediate ward from March to December 2020. A machine learning–based customized EWS algorithm, which incorporates demographics, laboratory values, vital signs, and comorbidities, and the NEWS, which uses vital signs only, were calculated at 12-hour intervals. These patients were retrospectively assessed for decompensation in the subsequent 12 or 24 hours, defined as death or transfer to an intensive care unit. Results Of 709 patients, 112 (15.8%) had a decompensation event. Using the custom EWS, decompensation within 12 and 24 hours was predicted with areas under the receiver operating curve (AUC) of 0.81 and 0.79, respectively. The NEWS score applied to the same population yielded AUCs of 0.83 and 0.81, respectively. The 24-hour negative predictive values (NPV) of the NEWS and EWS in patients identified as low risk were 99.6 and 99.2%, respectively. Conclusion The NEWS score performs as well as a customized EWS in COVID-19 patients, demonstrating the significance of vital signs in predicting outcomes. The relatively high positive predictive value and NPV of both scores are indispensable for optimally allocating clinical resources. In this relatively young, healthy population, a more complex score incorporating electronic health record data beyond vital signs does not add clinical benefit.