在异构的成年选择性手术患者群体中,为术后呼吸衰竭提供最少绝对收缩和选择操作员衍生的预测模型

Jacqueline C Stocking1, Sandra L Taylor1, Sili Fan1

  • 1Division of Pulmonary, Critical Care and Sleep Medicine (J. C. S., C. E. S., J. Y. A., N. J. K., and T. E. A.), Department of Internal Medicine, the Department of Public Health Sciences (S. L. T. and S. F.), the Outcomes Research Group (G. H. U.), Department of Surgery, University of California Davis, Sacramento, the Department of Anesthesiology and Perioperative Medicine (T. W. and M. C.), University of California Los Angeles, the Department of Medicine (M. K. O.), University of California Los Angeles, the VA Greater Los Angeles Healthcare System (M. K. O.), Los Angeles, the Department of Statistics (C. D.), University of California Davis, Davis, the Department of Anesthesia and Perioperative Care (J. M. A. and M. A. G.), University of California, San Francisco, San Francisco, the Department of Medicine (A. N. A.), University of California Irvine, Irvine, the Department of Surgery (R. A. M. and L. G.), University of California San Diego, San Diego, the College of Medicine (C. B. and J. M.), University of Arizona Health Sciences, the Department of Biomedical Engineering (V. S.), College of Engineering, the Center for Health Outcomes and PharmacoEconomic Research (I. A.), University of Arizona, Tucson, AZ, and The University of Florida-Scripps Research Institute (J. G. N. G.), Jupiter, FL.

CHEST critical care
|March 4, 2024
PubMed
概括

开发了一个预测模型,以识别患有术后呼吸衰竭 (PRF) 高风险的患者. 该模型使用电子健康记录数据来改进患者护理规划和资源分配.