Emma Cruz

All Publications

• Machine learning prediction of hospitalization costs for coronary artery bypass grafting operations. Surgery Cruz, E. O., Sakowitz, S., Mallick, S., Le, N., Chervu, N., Bakhtiyar, S. S., Benharash, P. 2024

  Abstract

  BACKGROUND: With the steady rise in health care expenditures, the examination of factors that may influence the costs of care has garnered much attention. Although machine learning models have previously been applied in health economics, their application within cardiac surgery remains limited. We evaluated several machine learning algorithms to model hospitalization costs for coronary artery bypass grafting.METHODS: All adult hospitalizations for isolated coronary artery bypass grafting were identified in the 2016 to 2020 Nationwide Readmissions Database. Machine learning models were trained to predict expenditures and compared with traditional linear regression. Given the significance of postoperative length of stay, we additionally developed models excluding postoperative length of stay to uncover other drivers of costs. To facilitate comparison, machine learning classification models were also trained to predict patients in the highest decile of costs. Significant factors associated with high cost were identified using SHapley Additive exPlanations beeswarm plots.RESULTS: Among 444,740 hospitalizations included for analysis, the median cost of hospitalization in coronary artery bypass grafting patients was $43,103. eXtreme Gradient Boosting most accurately predicted hospitalization costs, with R2= 0.519 over the validation set. The top predictive features in the eXtreme Gradient Boosting model included elective procedure status, prolonged mechanical ventilation, new-onset respiratory failure or myocardial infarction, and postoperative length of stay. After removing postoperative length of stay, eXtreme Gradient Boosting remained the most accurate model (R2= 0.38). Prolonged ventilation, respiratory failure, and elective status remained important predictive parameters.CONCLUSION: Machine learning models appear to accurately model total hospitalization costs for coronary artery bypass grafting. Future work is warranted to uncover other drivers of costs and improve the value of care in cardiac surgery.

  View details for DOI 10.1016/j.surg.2024.03.051

  View details for PubMedID 38760232

• Highly Excretable Gold Supraclusters for Translatable In Vivo Raman Imaging of Tumors. ACS nano Yu, J. H., Jeong, M. S., Cruz, E. O., Alam, I. S., Tumbale, S. K., Zlitni, A., Lee, S. Y., Park, Y. I., Ferrara, K., Kwon, S., Gambhir, S. S., Rao, J. 2023

  Abstract

  Raman spectroscopy provides excellent specificity for in vivo preclinical imaging through a readout of fingerprint-like spectra. To achieve sufficient sensitivity for in vivo Raman imaging, metallic gold nanoparticles larger than 10 nm were employed to amplify Raman signals via surface-enhanced Raman scattering (SERS). However, the inability to excrete such large gold nanoparticles has restricted the translation of Raman imaging. Here we present Raman-active metallic gold supraclusters that are biodegradable and excretable as nanoclusters. Although the small size of the gold nanocluster building blocks compromises the electromagnetic field enhancement effect, the supraclusters exhibit bright and prominent Raman scattering comparable to that of large gold nanoparticle-based SERS nanotags due to high loading of NIR-resonant Raman dyes and much suppressed fluorescence background by metallic supraclusters. The bright Raman scattering of the supraclusters was pH-responsive, and we successfully performed in vivo Raman imaging of acidic tumors in mice. Furthermore, in contrast to large gold nanoparticles that remain in the liver and spleen over 4 months, the supraclusters dissociated into small nanoclusters, and 73% of the administered dose to mice was excreted during the same period. The highly excretable Raman supraclusters demonstrated here offer great potential for clinical applications of in vivo Raman imaging.

  View details for DOI 10.1021/acsnano.2c10378

  View details for PubMedID 36688431