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

Fig. 1. The flowchart of our study. PACU, postoperative anesthesia care unit

Fig. 2. LASSO regression analysis for factors selection. A selection of the tuning parameter (λ) by using 10-fold via minimum and 1-SE criteria. B Non-zero coefficients selection by using the 10-fold cross-validation. LASSO, least absolute shrinkage, and selection operator

Fig. 3. A static nomogram showing a patient's scoring process. To use the nomogram, the specific points (black dots) of individual patients are located on each variable axis. A patient's characteristics are identified by a red dot. Red dots are drawn upward to determine the score received by each variable; the sum (3.38) of these score is located on the Total score axis, and a line is drawn downward to the PLOS axes to determine the probability (41.3%) of PLOS in PACU. ASA, American society of Anesthesiologists Physical Status; BMI, body mass index; PLOD, Prolonged length of operation duration; PLOS, prolonged length of stay

Fig. 4. AUC and calibration curves of the models. A, B AUC of the model10 and model11 to predict probability of PLOS in PACU in the development and validation sets. The black line (model11) represents AUC = 0.776. which is considered ideal. The red line (model10, based on which the nomogram was established) represents AUC = 0.773. AUC (model10) vs AUC (model11), p > 0.05. C, D Calibration curves of the nomogram for PLOS in the development and validation sets. The black dots calculated with nomogram are evenly distributed around the grey bold lines, showing perfect consistency. AUC, area under the receiver operating characteristic curve; DEV, development; ROC, receiver operating characteristic curve; PLOS, prolonged length of stay; VAD, validation

Fig. 5. DCA of the nomogram. A PLOS benefit in the development set. B PLOS benefit in the validation set. The blue line represents model10 (based on which the nomogram was established. The red line represents model11. The results of the prediction models are the blue and red lines. The two other lines are “intervention for all” (grey line) and “intervention for none” (black line). DCA, decision curve analysis; DEV, development; PLOS, prolonged length of stay; VAD, validation