Prediction of pacemaker-induced cardiomyopathy using a convolutional neural network based on clinical findings prior to pacemaker implantation

Abstract Risk factors for pacemaker-induced cardiomyopathy (PICM) have been previously reported, including a high burden of right ventricular pacing, lower left ventricular ejection fraction, a wide QRS duration, and left bundle branch block before pacemaker implantation (PMI). However, predicting the development of PICM remains challenging. This study aimed to use a convolutional neural network (CNN) model, based on clinical findings before PMI, to predict the development of PICM. Out of a total of 561 patients with dual-chamber PMI, 165 (mean age 71.6 years, 89 men [53.9%]) who underwent echocardiography both before and after dual-chamber PMI were enrolled. During a mean follow-up period of 1.7 years, 47 patients developed PICM. A CNN algorithm for prediction of the development of PICM was constructed based on a dataset prior to PMI that included 31 variables such as age, sex, body mass index, left ventricular ejection fraction, left ventricular end-diastolic diameter, left ventricular end-systolic diameter, left atrial diameter, severity of mitral regurgitation, severity of tricuspid regurgitation, ischemic heart disease, diabetes mellitus, hypertension, heart failure, New York Heart Association class, atrial fibrillation, the etiology of bradycardia (sick sinus syndrome or atrioventricular block) , right ventricular (RV) lead tip position (apex, septum, left bundle, His bundle, RV outflow tract), left bundle branch block, QRS duration, white blood cell count, haemoglobin, platelet count, serum total protein, albumin, aspartate transaminase, alanine transaminase, estimated glomerular filtration rate, sodium, potassium, C-reactive protein, and brain natriuretic peptide. The accuracy, sensitivity, specificity, and area under the curve of the CNN model were 75.8%, 55.6%, 83.3% and 0.78 respectively. The CNN model could accurately predict the development of PICM using clinical findings before PMI. This model could be useful for screening patients at risk of developing PICM, ensuring timely upgrades to physiological pacing to avoid missing the optimal intervention window

Temporary loss of perivascular aquaporin-4 in neocortex after transient middle cerebral artery occlusion in mice

The aquaporin-4 (AQP4) pool in the perivascular astrocyte membranes has been shown to be critically involved in the formation and dissolution of brain edema. Cerebral edema is a major cause of morbidity and mortality in stroke. It is therefore essential to know whether the perivascular pool of AQP4 is up- or down-regulated after an ischemic insult, because such changes would determine the time course of edema formation. Here we demonstrate by quantitative immunogold cytochemistry that the ischemic striatum and neocortex show distinct patterns of AQP4 expression in the reperfusion phase after 90 min of middle cerebral artery occlusion. The striatal core displays a loss of perivascular AQP4 at 24 hr of reperfusion with no sign of subsequent recovery. The most affected part of the cortex also exhibits loss of perivascular AQP4. This loss is of magnitude similar to that of the striatal core, but it shows a partial recovery toward 72 hr of reperfusion. By freeze fracture we show that the loss of perivascular AQP4 is associated with the disappearance of the square lattices of particles that normally are distinct features of the perivascular astrocyte membrane. The cortical border zone differs from the central part of the ischemic lesion by showing no loss of perivascular AQP4 at 24 hr of reperfusion but rather a slight increase. These data indicate that the size of the AQP4 pool that controls the exchange of fluid between brain and blood during edema formation and dissolution is subject to large and region-specific changes in the reperfusion phase