Gia Barboza
A Secondary Spatial Analysis of Gun Violence near Boston Schools: a Public Health Approach
Journal of urban health : bulletin of the New York Academy of Medicine
aimed analyze factors related delay transfer patients postanesthesia care unit pacu develop validate prediction model understanding factors guide precise clinical intervention collected data two cohorts 1153 297 patients underwent surgery treated pacu two time points examined clinical features anesthesia care data using analytical methods logistic regression random forest extreme gradient boosting xgboost screen variables establish prediction model validated simplified model plotted nomogram using lasso regression reduced dimensionality data developed multiple models plotted receiver operating characteristic roc calibration curves constructed simplified model pooling identified variables included hemoglobin hb alanine transaminase alt glucose levels duration anesthesia minimum bispectral index value bismin model good prediction performance parameters training validation sets auc 0909 08870932 training set 0939 09190959 validation set compared model 6 models net reclassification index nri integrated discriminant improvement idi index indicated differ significantly models developed scoring system showed good prediction performance verified training validation sets well external data additionally decision curve analysis dca clinical impact curve cic demonstrated potential clinical efficacy model guiding patient interventions predicting transfer delays postanesthesia care unit using predictive models feasible however merits exploration