Background: Laparoscopic cholecystectomy (LC) is a common surgical procedure for managing gallbladder disease. Prolonged length of stay (LOS) in the postanesthesia care unit (PACU) may lead to overcrowding and a decline in medical resource utilization. In this work, we aimed to develop and validate a predictive nomogram for identifying patients who require prolonged PACU LOS.Methods: Data from 913 patients undergoing LC at a single institution in China between 2018 and 2019 were collected, and grouped into a training set (456, cases during 2018) and a test set (457, cases during 2019). The definition of PACU LOS is the duration between admission to discharge from PACU, which we can derive from the electronic medical record system. Using the least absolute shrinkage and selection operator regression model, the optimal feature was selected, and multivariable logistic regression analysis was used to build the prolonged PACU LOS risk model. The C-index, calibration plot, and decision curve analysis (DCA) were used in assessing the model calibration, discrimination, and clinical application value, respectively. For external validation, the test set data was evaluated. Results:The predictive nomogram had 8 predictor variables for prolonged PACU LOS, including age, American Society of Anesthesiologists (ASA) grade, active smoker, gastrointestinal disease, liver disease, and cardiovascular disease. This model displayed efficient calibration and moderate discrimination with a C-index of 0.662 (95% confidence interval, 0.603 to 0.721) for the training set, and 0.609 (95% confidence interval, 0.549 to 0.669) for the test set. DCA demonstrated that the prolonged PACU LOS nomogram was reliable for clinical application when an intervention was decided at the possible threshold of 7%. Conclusions:We developed and validated a predictive nomogram with efficient calibration and moderate discrimination, and can be applied to identify patients most likely to be subjected to prolonged PACU LOS. This novel tool may shun overcrowding in PACU and optimize medical resource utilization.

Leukemia stem cells (LSCs), which evade standard chemotherapy, may lead to chemoresistance and disease relapse. The overexpression of ATP-binding cassette subfamily G member 2 (ABCG2) is an important determinant of drug resistance in LSCs and it can serve as a marker for LSCs. Targeting ABCG2 is a potential strategy to selectively treat and eradicate LSCs, and, hence, improve leukemia therapy. Tucatinib (Irbinitinib) is a novel tyrosine kinase inhibitor, targeting ErbB family member HER2, and was approved by the Food and Drug Administration in April 2020, and in Switzerland in May 2020 for the treatment of HER2-positive breast cancer. In the present study, the results demonstrated that tucatinib significantly improved the efficacy of conventional chemotherapeutic agents in ABCG2-overexpressing leukemia cells and primary leukemia blast cells, derived from patients with leukemia. In addition, tucatinib markedly decreased the proportion of leukemia stem cell-like side population (SP) cells. In SP cells, isolated from leukemia cells, the intracellular accumulation of Hoechst 33342, which is an ABCG2 substrate, was significantly elevated by tucatinib. Furthermore, tucatinib notably inhibited the efflux of [ 3 H]-mitoxantrone and, hence, there was a higher level of [ 3 H]-mitoxantrone in the HL60/ABCG2 cell line. The result from the ATPase assay revealed that tucatinib may interact with the drug substrate-binding site and stimulated ATPase activity of ABCG2. However, the protein expression level and cellular location of ABCG2 were not affected by tucatinib treatment. Taken together, these data suggested that tucatinib could sensitize conventional chemotherapeutic agents, in ABCG2-overexpressing leukemia cells and LSCs, by blocking the pump function of the ABCG2 protein. The present study revealed that combined treatment with tucatinib and conventional cytotoxic agents could be a potential therapeutic strategy in ABCG2-positive leukemia.

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