Browsing by Author "Bedoya, Armando D"

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    Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration.
    (MDM policy & practice, 2020-01-10) O'Brien, Cara; Goldstein, Benjamin A; Shen, Yueqi; Phelan, Matthew; Lambert, Curtis; Bedoya, Armando D; Steorts, Rebecca C
    Background. Identification of patients at risk of deteriorating during their hospitalization is an important concern. However, many off-shelf scores have poor in-center performance. In this article, we report our experience developing, implementing, and evaluating an in-hospital score for deterioration. Methods. We abstracted 3 years of data (2014-2016) and identified patients on medical wards that died or were transferred to the intensive care unit. We developed a time-varying risk model and then implemented the model over a 10-week period to assess prospective predictive performance. We compared performance to our currently used tool, National Early Warning Score. In order to aid clinical decision making, we transformed the quantitative score into a three-level clinical decision support tool. Results. The developed risk score had an average area under the curve of 0.814 (95% confidence interval = 0.79-0.83) versus 0.740 (95% confidence interval = 0.72-0.76) for the National Early Warning Score. We found the proposed score was able to respond to acute clinical changes in patients' clinical status. Upon implementing the score, we were able to achieve the desired positive predictive value but needed to retune the thresholds to get the desired sensitivity. Discussion. This work illustrates the potential for academic medical centers to build, refine, and implement risk models that are targeted to their patient population and work flow.
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    Machine learning for early detection of sepsis: an internal and temporal validation study.
    (JAMIA open, 2020-07) Bedoya, Armando D; Futoma, Joseph; Clement, Meredith E; Corey, Kristin; Brajer, Nathan; Lin, Anthony; Simons, Morgan G; Gao, Michael; Nichols, Marshall; Balu, Suresh; Heller, Katherine; Sendak, Mark; O'Brien, Cara

    Objective

    Determine if deep learning detects sepsis earlier and more accurately than other models. To evaluate model performance using implementation-oriented metrics that simulate clinical practice.

    Materials and methods

    We trained internally and temporally validated a deep learning model (multi-output Gaussian process and recurrent neural network [MGP-RNN]) to detect sepsis using encounters from adult hospitalized patients at a large tertiary academic center. Sepsis was defined as the presence of 2 or more systemic inflammatory response syndrome (SIRS) criteria, a blood culture order, and at least one element of end-organ failure. The training dataset included demographics, comorbidities, vital signs, medication administrations, and labs from October 1, 2014 to December 1, 2015, while the temporal validation dataset was from March 1, 2018 to August 31, 2018. Comparisons were made to 3 machine learning methods, random forest (RF), Cox regression (CR), and penalized logistic regression (PLR), and 3 clinical scores used to detect sepsis, SIRS, quick Sequential Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS). Traditional discrimination statistics such as the C-statistic as well as metrics aligned with operational implementation were assessed.

    Results

    The training set and internal validation included 42 979 encounters, while the temporal validation set included 39 786 encounters. The C-statistic for predicting sepsis within 4 h of onset was 0.88 for the MGP-RNN compared to 0.836 for RF, 0.849 for CR, 0.822 for PLR, 0.756 for SIRS, 0.619 for NEWS, and 0.481 for qSOFA. MGP-RNN detected sepsis a median of 5 h in advance. Temporal validation assessment continued to show the MGP-RNN outperform all 7 clinical risk score and machine learning comparisons.

    Conclusions

    We developed and validated a novel deep learning model to detect sepsis. Using our data elements and feature set, our modeling approach outperformed other machine learning methods and clinical scores.
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    The Eyes Have It-for Idiopathic Pulmonary Fibrosis: a Preliminary Observation.
    (Pulmonary therapy, 2022-09) Pleasants, Roy A; Bedoya, Armando D; Boggan, Joel M; Welty-Wolf, Karen; Tighe, Robert M

    Introduction

    The disease origins of idiopathic pulmonary fibrosis (IPF), which occurs at higher rates in certain races/ethnicities, are not understood. The highest rates occur in white persons of European descent, particularly those with light skin, who are also susceptible to lysosomal organelle dysfunction of the skin leading to fibroproliferative disease . We had observed clinically that the vast majority of patients with IPF had light-colored eyes, suggesting a phenotypic characteristic.

    Methods

    We pursued this observation through a research database from the USA Veterans Administration, a population that has a high occurrence of IPF due to predominance of elderly male smokers. Using this medical records database, which included facial photos, we compared the frequency of light (blue, green, hazel) and dark (light brown, brown) eyes among white patients diagnosed with IPF compared with a control group of lung granuloma only (no other radiologic evidence of interstitial lung disease).

    Results

    Light eye color was significantly more prevalent in patients with IPF than in the control group with lung granuloma [114/147 (77.6%) versus 129/263 (49.0%], p < 0.001), indicating that light-colored eyes are a phenotype associated with IPF .

    Conclusion

    We provide evidence that light eye color is predominant among white persons with IPF.