Background The interscalene nerve block provides analgesia for shoulder surgery, but is associated with diaphragm paralysis. One solution may be performing brachial plexus blocks more distally. This noninferiority study evaluated analgesia for blocks at the supraclavicular and anterior suprascapular levels, comparing them individually to the interscalene approach. Methods One hundred-eighty-nine subjects undergoing arthroscopic shoulder surgery were recruited to this double-blind trial and randomized to interscalene, supraclavicular, or anterior suprascapular block using 15 ml, 0.5% ropivacaine. The primary outcome was numeric rating scale pain scores analyzed using noninferiority testing. The predefined noninferiority margin was one point on the 11-point pain scale. Secondary outcomes included opioid consumption and pulmonary assessments. Results All subjects completed the study through the primary outcome analysis. Mean pain after surgery was: interscalene = 1.9 (95% CI, 1.3 to 2.5), supraclavicular = 2.3 (1.7 to 2.9), suprascapular = 2.0 (1.4 to 2.6). The primary outcome, mean pain score difference of supraclavicular–interscalene was 0.4 (–0.4 to 1.2; P = 0.088 for noninferiority) and of suprascapular–interscalene was 0.1 (–0.7 to 0.9; P = 0.012 for noninferiority). Secondary outcomes showed similar opioid consumption with better preservation of vital capacity in the anterior suprascapular group (90% baseline [P < 0.001]) and the supraclavicular group (76% [P = 0.002]) when compared to the interscalene group (67%). Conclusions The anterior suprascapular block, but not the supraclavicular, provides noninferior analgesia compared to the interscalene approach for major arthroscopic shoulder surgery. Pulmonary function is best preserved with the anterior suprascapular nerve block.

A continuous suprascapular block may be a useful analgesic alternative to the interscalene or supraclavicular approaches when the preservation of lung function is a priority after shoulder replacement surgery.

ClinicalTrials.gov (NCT02701114).

BackgroundAdvanced 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.MethodsThirty 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.ResultsFactors 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).ConclusionsANN 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.

NCT02785055 (https://clinicaltrials.gov/).