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Accurate preoperative prediction of unplanned, 30-day postoperative readmission using 8 predictor variables: Methodological issues

Published:September 13, 2019DOI:https://doi.org/10.1016/j.surg.2019.07.029
      We would like to thank Gibula et al for their study developing the accurate preoperative prediction of unplanned, 30-day postoperative readmission using 8 predictor variables.
      • Gibula D.R.
      • Singh A.B.
      • Bronsert M.R.
      • et al.
      Accurate preoperative prediction of unplanned 30-day postoperative readmission using 8 predictor variables.
      They were pursuing this goal in their study that developed and implemented the Surgical Risk Preoperative Assessment System (SURPAS), which uses just 8 preoperative predictor variables to estimate the risk of 11 adverse outcomes across a broad spectrum of surgical specialties. Preliminary analyses were performed using 28 preoperative variables to determine which factors showed a bivariable association with unplanned, related 30-day hospital readmission. The bivariable association between each of these variables and unplanned, related 30-day readmission was tested using a χ2 test for categorical predictor variables or an unpaired t test for continuous variables. This model, as the full model, was compared with the 8-variable model. They used a logistic regression model for both subsets of variables and a comparison of results was performed on the basis of using the C-index as a measure of discrimination, the Hosmer-Lemeshow observed-to-expected plots as a measure of calibration, and the Brier score, a combined metric of discrimination and calibration. The result showed that an 8 variable SURPAS model detects patients at risk for postoperative, unplanned, related readmission as accurately as the full model developed from all 28 nonlaboratory preoperative variables.
      • Gibula D.R.
      • Singh A.B.
      • Bronsert M.R.
      • et al.
      Accurate preoperative prediction of unplanned 30-day postoperative readmission using 8 predictor variables.
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