Bio

Clinical Focus


  • Pulmonary Disease

Academic Appointments


Professional Education


  • Residency: Albert Einstein Medical Center Internal Medicine Residency (2014) PA
  • Board Certification: Critical Care Medicine, American Board of Internal Medicine (2019)
  • Fellowship: Stanford University Pulmonary and Critical Care Fellowship (2019) CA
  • Board Certification: Internal Medicine, American Board of Internal Medicine (2013)
  • Medical Education: University of Witwatersrand (2006) South Africa
  • Fellowship, Stanford University Pulmonary and Critical Care Fellowship (2019)
  • Board Certification, Pulmonary Disease, American Board of Internal Medicine (2018)
  • Chief Residency, Albert Einstein Medical Center Philadelphia (2014)
  • Board Certification, Internal Medicine, American Board of Internal Medicine (2013)
  • Residency, Albert Einstein Medical Center Philadelphia (2013)
  • Medical Education, University of Witwatersrand (2006)

Publications

All Publications


  • Machine learning algorithms to differentiate among pulmonary complications after hematopoietic cell transplant. Chest Sharifi, H., Lai, Y. K., Guo, H., Hoppenfeld, M., Guenther, Z. D., Johnston, L., Brondstetter, T., Chhatwani, L., Nicolls, M. R., Hsu, J. L. 2020

    Abstract

    Pulmonary complications, including infections, are highly prevalent in patients after hematopoietic cell transplant with chronic graft-versus-host disease. These comorbid diseases can make the diagnosis of early lung graft-versus-host disease (bronchiolitis obliterans syndrome) challenging. A quantitative method to differentiate among these pulmonary diseases can address diagnostic challenges and facilitate earlier and more targeted therapy.We conducted a single center study of 66 patients with computed tomography chest scans analyzed with a quantitative imaging tool known as parametric response mapping. Parametric response mapping results were correlated with pulmonary function tests and clinical characteristics. Five parametric response mapping metrics were applied to K-means clustering and support vector machine models to distinguish among post-transplant lung complications solely from quantitative output.Compared to parametric response mapping, spirometry showed a moderate correlation with radiographic air trapping, and total lung capacity and residual volume showed a strong correlation with radiographic lung volumes. K-means clustering analysis distinguished 4 unique clusters. Clusters 2 and 3 represented obstructive physiology (encompassing 81% of patients with bronchiolitis obliterans syndrome) in increasing severity (percent air trapping 15.6% and 43.0%, respectively). Cluster 1 was dominated by normal lung, and cluster 4 was characterized by patients with parenchymal opacities. A support vector machine algorithm differentiated bronchiolitis obliterans syndrome with specificity of 88%, sensitivity of 83%, accuracy of 86% and an area under the receiver operating characteristic curve of 0.85.Our machine learning models offer a quantitative approach for the identification of bronchiolitis obliterans syndrome versus other lung diseases, including late pulmonary complications after hematopoietic cell transplant.

    View details for DOI 10.1016/j.chest.2020.02.076

    View details for PubMedID 32343962

  • A Young Woman With a Rapidly GrowingThoracic Tumor. Chest Lai, Y. K., Holmes, B., Guo, H. H. 2019; 155 (5): e145?e148

    Abstract

    CASE PRESENTATION: A 38-year-old woman presented with 2months of dry cough, progressiveshortness of breath, central chest pain, nausea, vomiting, and dizziness. She was previously healthy and was not taking any medications. She denied fever, night sweats, or weight loss. She had a two pack-year smoking history and had quit smoking at 27 years of age. She denied drug use and had no recent travel history. Family history was pertinent for ovarian cancer, breast cancer, and colon cancer.

    View details for DOI 10.1016/j.chest.2018.12.014

    View details for PubMedID 31060712

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