ABSTRACT
INTRODUCTION: Neuraxial labor analgesia has become an integral part of modern obstetric anesthetic practice. Presence of a familiar person during its placement may be beneficial to the patient. A survey was sent to anesthesiologists practicing obstetric anesthesia in the USA to determine their views. METHODS: The survey queried the following: existence of a written policy; would they allow a visitor; visitor's view, sitting or standing; reasons to allow or not allow a visitor; and influence by other staff on the decision. The responses were analyzed using multiple chi-square analyses. RESULTS: Most practitioners supported allowing a visitor during placement. Reduction of patient anxiety and fulfillment of patient request were the major reasons for allowing a visitor. Sitting position and no view of the workspace were preferred. Visitor interference and safety were cited as the major reasons for precluding a visitor. Nonanesthesia providers rarely influenced the decision. Epidural analgesia was the preferred technique. Essentially no bias was found in the responses; there was statistical uniformity regardless of procedures done per week, years in practice, professional certification, geographic region (rural, urban, or suburban), or academic, private, or government responders. CONCLUSION: The practice of visitor presence during the placement of neuraxial labor analgesia is gaining acceptance.
ABSTRACT
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.
