A novel nomogram for predicting the prolonged length of stay in post-anesthesia care unit after selective operation.

Abstract

Background Prolonged length of stay (PLOS) in post-anesthesia care unit (PACU) is a combination of risk factors and complications that can compromise quality of care and operating room efficiency. Our study aimed to develop a nomogram to predict PLOS of patients undergoing elective surgery. Methods Data from 24017 patients were collected. Least absolute shrinkage and selection operator (LASSO) was used to screen variables. A logistic regression model was built on variables determined by a combined method of forward selection and backward elimination. Nomogram was designed with the model. The nomogram performance was evaluated with the area under the receiver operating characteristic curve (AUC) for discrimination, calibration plot for consistency between predictions and actuality, and decision curve analysis (DCA) for clinical application value. Results A nomogram was established based on the selected ten variables, including age, BMI < 21 kg/m2, American society of Anesthesiologists Physical Status (ASA), surgery type, chill, delirium, pain, naloxone, operation duration and blood transfusion. The C-index value was 0.773 [95% confidence interval (CI) = 0.765–0.781] in the development set and 0.757 (95% CI = 0.744–0.770) in the validation set. The AUC was > 0.75 for the prediction of PLOS. The calibration curves revealed high consistencies between the predicted and actual probability. The DCA showed that if the threshold probability is over 10%, using the models to predict PLOS and implement intervention adds more benefit. Conclusions This study presented a nomogram to facilitate individualized prediction of PLOS patients undergoing elective surgery.

Development and Validation of a Clinical Model for Predicting Delay in Postoperative Transfer Out of the Post-Anesthesia Care Unit: A Retrospective Cohort Study

“…In the study of Zhang et al 6 delayed recovery of PACU after surgery was associated with old age, neurosurgery, long duration of anesthesia, ASA grade III, antibiotic use during surgery, and postoperative analgesia. These findings provide predictors of delayed recovery from PACU, particularly neurosurgery and old age, whereas in Fang et al, 7 based on ten selected variables, A predictive model was developed including age, BMI < 21 kg/m2, American Society of Anesthesiologists Physical condition (ASA), type of surgery, chills, dementia, pain, naloxone, duration of surgery, and blood transfusion. The predictive model incorporates data on the patient's physical condition, surgery-related conditions, patient's mental state, pain, and anesthetic antagonists.…”

“…5 Currently, some clinical models for predicting the delayed recovery or prolonged length of stay in PACU among different populations have been reported. 6,7 However, the population of these studies was limited to outpatients or patients with a certain type of surgery, and the sample size was small.…”