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A Least Absolute Shrinkage and Selection Operator-Derived Predictive Model for Postoperative Respiratory Failure in a
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.
Background:
Postoperative respiratory failure (PRF) is associated with increased hospital charges and worse patient outcomes. Reliable prediction models can help to guide postoperative planning to optimize care, to guide resource allocation, and to foster shared decision-making with patients.
Research Question:
Can a predictive model be developed to accurately identify patients at high risk of PRF?
Study Design And Methods:
In this single-site proof-of-concept study, we used structured query language to extract, transform, and load electronic health record data from 23,999 consecutive adult patients admitted for elective surgery (2014-2021). Our primary outcome was PRF, defined as mechanical ventilation after surgery of > 48 h. Predictors of interest included demographics, comorbidities, and intraoperative factors. We used logistic regression to build a predictive model and the least absolute shrinkage and selection operator procedure to select variables and to estimate model coefficients. We evaluated model performance using optimism-corrected area under the receiver operating curve and area under the precision-recall curve and calculated sensitivity, specificity, positive and negative predictive values, and Brier scores.
Results:
Two hundred twenty-five patients (0.94%) demonstrated PRF. The 18-variable predictive model included: operations on the cardiovascular, nervous, digestive, urinary, or musculoskeletal system; surgical specialty orthopedic (nonspine); Medicare or Medicaid (as the primary payer); race unknown; American Society of Anesthesiologists class ≥ III; BMI of 30 to 34.9 kg/m2; anesthesia duration (per hour); net fluid at end of the operation (per liter); median intraoperative FIO2, end title CO2, heart rate, and tidal volume; and intraoperative vasopressor medications. The optimism-corrected area under the receiver operating curve was 0.835 (95% CI,0.808-0.862) and the area under the precision-recall curve was 0.156 (95% CI, 0.105-0.203).
Interpretation:
This single-center proof-of-concept study demonstrated that a structured query language extract, transform, and load process, based on readily available patient and intraoperative variables, can be used to develop a prediction model for PRF. This PRF prediction model is scalable for multicenter research. Clinical applications include decision support to guide postoperative level of care admission and treatment decisions.
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