In response to recent studies, we investigated an association between perioperative β-blockade and breast cancer metastases. First, a retrospective study examining perioperative β-blocker use and cancer recurrence and metastases was conducted on 1,029 patients who underwent breast cancer surgery at the City of Hope Cancer Center between 2000 and 2010. We followed the clinical study and examined proliferation, migration, and invasion in vitro of primary and brain-metastatic breast cancer cells in response to β2-activation and inhibition. We also investigated in vivo the metastatic potential of propranolol-treated metastatic cells. For stage II breast cancer patients, perioperative β-blockade was associated with decreased cancer recurrence using Cox regression analysis (hazard's ratio =0.51; 95% CI: 0.23–0.97; p=0.041). Triple-negative (TN) brain-metastatic cells were found to have increased β2-adrenergic receptor mRNA and protein expression relative to TN primary cells. In response to β2-adrenergic receptor activation, TN brain-metastatic cells also exhibited increased cell proliferation and migration relative to the control. These effects were abrogated by propranolol. Propranolol decreased β2-adrenergic receptor-activated invasion. In vivo, propranolol treatment of TN brain-metastatic cells decreased establishment of brain metastases. Our results suggest that stress and corresponding β2-activation may promote the establishment of brain metastases of TN breast cancer cells. In addition, our data suggest a benefit to perioperative β-blockade during surgery-induced stress with respect to breast cancer recurrence and metastases.

The da Vinci Xi can be safely assimilated into a surgical oncology program. The Xi offers versatility to various oncologic procedures with satisfactory complication and readmission rates.

Surgeries performed with traditionally available robotic systems have many well-documented anesthetic implications. In this observational report, new and unique anesthetic considerations encountered with the introduction of the da Vinci Xi robot related to positioning operating room equipment, patient access and chance for unintended patient contact are described.

The administration of hyperthermic intraperitoneal chemotherapy (HIPEC) is often associated with significant intraoperative electrolyte changes. We retrospectively examined the pre-HIPEC and post-HIPEC intraoperative basic metabolic panel (

Background. The American Society of Anesthesiologists (ASA) Physical Status Classification System defines peri-operative patient scores as 1 (healthy) thru 6 (brain dead). The scoring is used by the anesthesiologists to classify surgical patients based on co-morbidities and various clinical characteristics. The classification is always done by an anesthesiologist prior operation. There is a variability in scoring stemming from individual experiences / biases of the scoring anesthesiologists, which impacts prediction of operating times, length of stay in the hospital, necessity of blood transfusion, etc. In addition, the score affects anesthesia coding and billing. It is critical to remove subjectivity from the process to achieve reproducible generalizable scoring. Methods. A machine learning (ML) approach was used to associate assigned ASA scores with peri-operative patients' clinical characteristics. More than ten ML algorithms were simultaneously trained, validated, and tested with retrospective records. The most accurate algorithm was chosen for a subsequent test on an independent dataset. DataRobot platform was used to run and select the ML algorithms. Manual scoring was also performed by one anesthesiologist. Intra-class correlation coefficient (ICC) was calculated to assess the consistency of scoring Results. Records of 19,095 procedures corresponding to 12,064 patients with assigned ASA scores by 17 City of Hope anesthesiologists were used to train a number of ML algorithms (DataRobot platform). The most accurate algorithm was tested with independent records of 2325 procedures corresponding to 1999 patients. In addition, 86 patients from the same dataset were scored manually. The following ICC values were computed: COH anesthesiologists vs. ML - 0.427 (fair); manual vs. ML - 0.523 (fair-to-good); manual vs. COH anesthesiologists - 0.334 (poor). Conclusions. We have shown the feasibility of using ML for assessing the ASA score. In principle, a group of experts (i.e. physicians, institutions, etc.) can train the ML algorithm such that individual experiences and biases would cancel each leaving the objective ASA score intact. As more data are being collected, a valid foundation for refinement to the ML will emerge.