Aims: Left bundle branch pacing (LBBP) recently emerges as a novel pacing modality. We aimed to evaluate the feasibility and cardiac synchrony of permanent LBBP in bradycardia patients. Methods and results: Left bundle branch pacing was successfully performed in 56 pacemaker-indicated patients with normal cardiac function. Left bundle branch pacing was achieved by penetrating the interventricular septum (IVS) into the left side sub-endocardium with the pacing lead. His-bundle pacing (HBP) was successfully performed in another 29 patients, 19 of whom had right ventricular septal pacing (RVSP) for backup pacing. The QRS duration, left ventricular (LV) activation time (LVAT), and mechanical synchrony using phase analysis of gated SPECT myocardial perfusion imaging were evaluated. Paced QRS duration in LBBP group was significantly shorter than that in RVSP group (117.8 ± 11.0 ms vs. 158.1 ± 11.1 ms, P < 0.0001) and wider than that in HBP group (99.7 ± 15.6 ms, P < 0.0001). Left bundle branch potential was recorded during procedure in 37 patients (67.3%). Left bundle branch pacing patients with potential had shorter LVAT than those without potential (73.1 ± 11.3 ms vs. 83.2 ± 16.8 ms, P = 0.03). Left bundle branch pacing patients with potential had similar LV mechanical synchrony to those in HBP group. R-wave amplitude and capture threshold of LBBP were 17.0 ± 6.7 mV and 0.5 ± 0.1 V, respectively at implant and remained stable during a mean follow-up of 4.5 months without lead-related complications. Conclusion: Permanent LBBP through IVS is safe and feasible in bradycardia patients. Left bundle branch pacing could achieve favourable cardiac electrical and LV mechanical synchrony.

Objective. The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire. Methods. We enrolled 5,272 individuals who filled out a 37-item questionnaire. In order to select the most important features, three different techniques of feature selection were tested. Then, the top features combined with six classification algorithms were used to develop the diagnostic models. Results. Information Gain was the most effective among the three feature selection methods. The Naive Bayes algorithm performed the best (accuracy = 0.81, precision = 0.82, recall = 0.81, and F-measure = 0.81) among the six classification models. Conclusion. The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia.

Using machine learning techniques, we developed a brief questionnaire to aid neurologists and neuropsychologists in the screening of mild cognitive impairment (MCI) and dementia. With the reduction of the survey size as a goal of this research, feature selection based on information gain was performed to rank the contribution of the 45 items corresponding to patient responses to the specified questions. The most important items were used to build the optimal screening model based on the accuracy, practicality, and interpretability. The diagnostic accuracy for discriminating normal cognition (NC), MCI, very mild dementia (VMD) and dementia was validated in the test group. The screening model (NMD-12) was constructed with the 12 items that were ranked the highest in feature selection. The receiver-operator characteristic (ROC) analysis showed that the area under the curve (AUC) in the test group was 0.94 for discriminating NC vs. MCI, 0.88 for MCI vs. VMD, 0.97 for MCI vs. dementia, and 0.96 for VMD vs. dementia, respectively. The NMD-12 model has been developed and validated in this study. It provides healthcare professionals with a simple and practical screening tool which accurately differentiates NC, MCI, VMD, and dementia.