Atrial fibrillation after cardiac surgery: A systematic review and meta-analysis

ObjectiveNew-onset postoperative atrial fibrillation (POAF) after cardiac surgery is common, with rates up to 60%. POAF has been associated with early and late stroke, but its association with other cardiovascular outcomes is less known. The objective was to perform a meta-analysis of the studies reporting the association of POAF with perioperative and long-term outcomes in patients with cardiac surgery.MethodsWe performed a systematic review and a meta-analysis of studies that presented outcomes for cardiac surgery on the basis of the presence or absence of POAF. MEDLINE, EMBASE, and the Cochrane Library were assessed; 57 studies (246,340 patients) were selected. Perioperative mortality was the primary outcome. Inverse variance method and random model were performed. Leave-one-out analysis, subgroup analyses, and metaregression were conducted.ResultsPOAF was associated with perioperative mortality (odds ratio [OR], 1.92; 95% confidence interval [CI], 1.58-2.33), perioperative stroke (OR, 2.17; 95% CI, 1.90-2.49), perioperative myocardial infarction (OR, 1.28; 95% CI, 1.06-1.54), perioperative acute renal failure (OR, 2.74; 95% CI, 2.42-3.11), hospital (standardized mean difference, 0.80; 95% CI, 0.53-1.07) and intensive care unit stay (standardized mean difference, 0.55; 95% CI, 0.24-0.86), long-term mortality (incidence rate ratio [IRR], 1.54; 95% CI, 1.40-1.69), long-term stroke (IRR, 1.33; 95% CI, 1.21-1.46), and longstanding persistent atrial fibrillation (IRR, 4.73; 95% CI, 3.36-6.66).ConclusionsThe results suggest that POAF after cardiac surgery is associated with an increased occurrence of most short- and long-term cardiovascular adverse events. However, the causality of this association remains to be established.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

In Vitro-In Vivo Extrapolation and Hepatic Clearance-Dependent Underprediction

Accurately predicting the hepatic clearance of compounds using in vitro to in vivo extrapolation (IVIVE) is crucial within the pharmaceutical industry. However, several groups have recently highlighted the serious error in the process. Although empirical or regression-based scaling factors may be used to mitigate the common underprediction, they provide unsatisfying solutions because the reasoning behind the underlying error has yet to be determined. One previously noted trend was intrinsic clearance-dependent underprediction, highlighting the limitations of current in vitro systems. When applying these generated in vitro intrinsic clearance values during drug development and making first-in-human dose predictions for new chemical entities though, hepatic clearance is the parameter that must be estimated using a model of hepatic disposition, such as the well-stirred model. Here, we examine error across hepatic clearance ranges and find a similar hepatic clearance-dependent trend, with high clearance compounds not predicted to be so, demonstrating another gap in the field.     

Real-time refinement of subthalamic nucleus targeting using Bayesian decision-making on the root mean square measure.

The subthalamic nucleus (STN) is a major target for treatment of advanced Parkinson's disease patients undergoing deep brain stimulation surgery. Microelectrode recording (MER) is used in many cases to identify the target nucleus. A real-time procedure for identifying the entry and exit points of the STN would improve the outcome of this targeting procedure. We used the normalized root mean square (NRMS) of a short (5 seconds) MER sampled signal and the estimated anatomical distance to target (EDT) as the basis for this procedure. Electrode tip location was defined intraoperatively by an expert neurophysiologist to be before, within, or after the STN. Data from 46 trajectories of 27 patients were used to calculate the Bayesian posterior probability of being in each of these locations, given RMS-EDT pair values. We tested our predictions on each trajectory using a bootstrapping technique, with the rest of the trajectories serving as a training set and found the error in predicting the STN entry to be (mean +/- SD) 0.18 +/- 0.84, and 0.50 +/- 0.59 mm for STN exit point, which yields a 0.30 +/- 0.28 mm deviation from the expert's target center. The simplicity and computational ease of RMS calculation, its spike sorting-independent nature and tolerance to electrode parameters of this Bayesian predictor, can lead directly to the development of a fully automated intraoperative physiological procedure for the refinement of imaging estimates of STN borders.     

磁场对家兔急性实验性高血压的影响

目的 :探讨内关穴敷磁的降压效应。方法 :40只清醒家兔和 40只麻醉家兔 (其中 2 0只去减压神经 ) ,静脉注射去甲肾上腺素或苯肾上腺素引起急性实验性高血压 ,并于内关穴敷磁 (0 .1～ 0 .2T) ,记录血压、心率和敷磁前及 1h后的血液粘度的变化。结果 :敷磁 1h后各实验组平均动脉压 (MAP)显著下降 (P <0 .0 1)。麻醉和清醒家兔MAP降幅无差异 (P >0 .0 5 ) ,但显著高于去减压神经家兔MAP的降幅 (P <0 .0 1或 0 .0 5 ) ,各实验组全血比粘度显著下降 (P <0 .0 1或 0 .0 5 ) ,实验组与各自对照组心率敷磁前后无差异 (P >0 .0 5 )。结论 :磁场具有降压作用 ,但对心率无影响 ,其降压效应与减压神经的抑制和全血粘度的降低有关 ,与化学感受性升压反射无关