Noteworthy Literature Published in 2017 for Thoracic Transplantation Anesthesiologists.

Thoracic organ transplantation constitutes a significant proportion of all transplant procedures. Thoracic solid organ transplantation continues to be a burgeoning field of research. This article presents a review of remarkable literature published in 2017 regarding perioperative issues pertinent to the thoracic transplant anesthesiologists.

ASA physical status assignment by non-anesthesia providers: Do surgeons consistently downgrade the ASA score preoperatively?

OBJECTIVE: The American Society of Anesthesiologists physical status (ASA-PS) is associated with increased morbidity and mortality in the perioperative period. When surgeries are scheduled by surgeons and their staff at our large institution a presumed ASA-PS is chosen. This is because our institution (and, anecdotally, others in our hospital system and elsewhere), recognizing the relationship between higher ASA-PS and poorer postoperative outcomes, requires all patients with higher ASA-PS levels (≥3) to undergo enhanced preoperative workup. The patients may not, however, necessarily be seen in the anesthesia clinic prior to surgery. As a result, patients are assigned a presumed ASA-PS by a non-anesthesia provider (e.g., surgeons and physician extenders) that may not reflect the ASA-PS chosen by the anesthesiologist on the day of surgery. Errors in the accuracy of the ASA-PS prior to surgery lead to unnecessary and costly preoperative testing, delays in operative procedures, and potential case cancellations. Our study aimed to determine whether there are significant differences in the assignment of ASA-PS by non-anesthesia providers when compared to anesthesia providers. DESIGN: We administered an IRB-approved survey asking the ASA-PS of 20 hypothetical case vignettes to 229 clinicians in various departments. Responses by non-anesthesia providers were compared to the consensus of the department of anesthesiology. SETTING: Faculty office spaces and conferences. PATIENTS: No patients, physicians only. INTERVENTIONS: Survey administration. MEASUREMENTS: ASA-PS scores acquired from surveys. MAIN RESULTS: Residents and faculty in the department of anesthesiology demonstrated no statistical difference in the median ASA score in 19/20 case scenarios. All other departments were statistically different when compared to the department of anesthesiology (p<0.05). The probability of a department either over- or under-rating the ASA-PS was calculated, and is summarized in Fig. 3. All departments, except anesthesiology, had a 30-40% chance of under-rating the ASA-PS of the patients in the clinical vignettes. CONCLUSIONS: Non-anesthesia providers assign ASA-PS with significantly less accuracy than do anesthesia providers, even when adjusted for multiple comparisons. Surgical and procedural departments were found to consistently under-rate the ASA-PS of patients in our clinical vignettes.

Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables.

BACKGROUND: Advanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics. METHODS: Thirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor ("trained" data) were then applied to data for a "new" patient to predict ICU LOS for that individual. RESULTS: Factors identified in the ALM model were: use of an intra-aortic balloon pump; O2 delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO2. The r2 value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p <0.0001. Cross validation in prediction of a "new" patient yielded r2 = 0.200, p <0.0001. The same 8 factors analyzed with ANN yielded a training prediction r2 of 0.535 (p <0.0001) and a cross validation prediction r2 of 0.410, p <0.0001. Two additional predictive algorithms were studied, but they had lower prediction accuracies. Our validated neural network model identified the upper quartile of ICU LOS with an odds ratio of 9.8(p <0.0001). CONCLUSIONS: ANN demonstrated a 2-fold greater accuracy than ALM in prediction of observed ICU LOS. This greater accuracy would be presumed to result from the capacity of ANN to capture nonlinear effects and higher order interactions. Predictive modeling may be of value in early anticipation of risks of post-operative morbidity and utilization of ICU facilities.