Browsing by Author "Olson, Andrew"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Machine learning functional impairment classification with electronic health record data.
    (Journal of the American Geriatrics Society, 2023-09) Pavon, Juliessa M; Previll, Laura; Woo, Myung; Henao, Ricardo; Solomon, Mary; Rogers, Ursula; Olson, Andrew; Fischer, Jonathan; Leo, Christopher; Fillenbaum, Gerda; Hoenig, Helen; Casarett, David

    Background

    Poor functional status is a key marker of morbidity, yet is not routinely captured in clinical encounters. We developed and evaluated the accuracy of a machine learning algorithm that leveraged electronic health record (EHR) data to provide a scalable process for identification of functional impairment.

    Methods

    We identified a cohort of patients with an electronically captured screening measure of functional status (Older Americans Resources and Services ADL/IADL) between 2018 and 2020 (N = 6484). Patients were classified using unsupervised learning K means and t-distributed Stochastic Neighbor Embedding into normal function (NF), mild to moderate functional impairment (MFI), and severe functional impairment (SFI) states. Using 11 EHR clinical variable domains (832 variable input features), we trained an Extreme Gradient Boosting supervised machine learning algorithm to distinguish functional status states, and measured prediction accuracies. Data were randomly split into training (80%) and test (20%) sets. The SHapley Additive Explanations (SHAP) feature importance analysis was used to list the EHR features in rank order of their contribution to the outcome.

    Results

    Median age was 75.3 years, 62% female, 60% White. Patients were classified as 53% NF (n = 3453), 30% MFI (n = 1947), and 17% SFI (n = 1084). Summary of model performance for identifying functional status state (NF, MFI, SFI) was AUROC (area under the receiving operating characteristic curve) 0.92, 0.89, and 0.87, respectively. Age, falls, hospitalization, home health use, labs (e.g., albumin), comorbidities (e.g., dementia, heart failure, chronic kidney disease, chronic pain), and social determinants of health (e.g., alcohol use) were highly ranked features in predicting functional status states.

    Conclusion

    A machine learning algorithm run on EHR clinical data has potential utility for differentiating functional status in the clinical setting. Through further validation and refinement, such algorithms can complement traditional screening methods and result in a population-based strategy for identifying patients with poor functional status who need additional health resources.
  • Thumbnail Image
    ItemOpen Access
    Microglia are effector cells of CD47-SIRPα antiphagocytic axis disruption against glioblastoma.
    (Proceedings of the National Academy of Sciences of the United States of America, 2019-01) Hutter, Gregor; Theruvath, Johanna; Graef, Claus Moritz; Zhang, Michael; Schoen, Matthew Kenneth; Manz, Eva Maria; Bennett, Mariko L; Olson, Andrew; Azad, Tej D; Sinha, Rahul; Chan, Carmel; Assad Kahn, Suzana; Gholamin, Sharareh; Wilson, Christy; Grant, Gerald; He, Joy; Weissman, Irving L; Mitra, Siddhartha S; Cheshier, Samuel H
    Glioblastoma multiforme (GBM) is a highly aggressive malignant brain tumor with fatal outcome. Tumor-associated macrophages and microglia (TAMs) have been found to be major tumor-promoting immune cells in the tumor microenvironment. Hence, modulation and reeducation of tumor-associated macrophages and microglia in GBM is considered a promising antitumor strategy. Resident microglia and invading macrophages have been shown to have distinct origin and function. Whereas yolk sac-derived microglia reside in the brain, blood-derived monocytes invade the central nervous system only under pathological conditions like tumor formation. We recently showed that disruption of the SIRPα-CD47 signaling axis is efficacious against various brain tumors including GBM primarily by inducing tumor phagocytosis. However, most effects are attributed to macrophages recruited from the periphery but the role of the brain resident microglia is unknown. Here, we sought to utilize a model to distinguish resident microglia and peripheral macrophages within the GBM-TAM pool, using orthotopically xenografted, immunodeficient, and syngeneic mouse models with genetically color-coded macrophages (Ccr2 RFP) and microglia (Cx3cr1 GFP). We show that even in the absence of phagocytizing macrophages (Ccr2 RFP/RFP), microglia are effector cells of tumor cell phagocytosis in response to anti-CD47 blockade. Additionally, macrophages and microglia show distinct morphological and transcriptional changes. Importantly, the transcriptional profile of microglia shows less of an inflammatory response which makes them a promising target for clinical applications.