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Machine-learning statistical model from EHR data can be useful to predict surgical complications. The combination of EHR extracted free text, blood samples values, and patient vital signs, improves the model performance. These results can be used as a framework for preoperative clinical decision support.

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ICD Electrograms and Origin of Impulses. Introduction:The implantable cardioverterdefibrillator (ICD) electrogram (EG) is a documentation of ventricular tachycardia. We prospectively analyzed EGs from ICD electrodes located at the right ventricle apex to establish (1) ability to regionalize origin of left ventricle (LV) impulses, and (2) spatial resolution to distinguish between paced sites. Methods and Results: LV electro-anatomic maps were generated in 15 patients. ICD-EGs were recorded during pacing from 22 ± 10 LV sites. Voltage of far-field EG deflections (initial, peak, final) and time intervals between far-field and bipolar EGs were measured. Blinded visual analysis was used for spatial resolution. Initial deflections were more negative and initial/peak ratios were larger for lateral versus septal and superior versus inferior sites. Time intervals were shorter for apical versus basal and septal versus lateral sites. Best predictive cutoff values were voltage of initial deflection <-1.24 mV, and initial/peak ratio >0.45 for a lateral site, voltage of final deflection <-0.30 for an inferior site, and time interval <80 milliseconds for an apical site. In a subsequent group of 9 patients, these values predicted correctly paced site location in 54-75% and tachycardia exit site in 60-100%. Recognition of paced sites as different by EG inspection was 91% accurate. Sensitivity increased with distance (0.96 if ≥ 2 cm vs 0.84 if < 2 cm, P < 0.001) and with presence of low-voltage tissue between sites (0.94 vs 0.88, P < 0.001). Conclusions: Standard ICD-EG analysis can help regionalize LV sites of impulse formation. It can accurately distinguish between 2 sites of impulse formation if they are ≥2 cm apart. (J Cardiovasc Electrophysiol, catheter ablation, electroanatomical mapping, electrogram, implantable defibrillator, pace-mapping, ventricular tachycardia

The presence of bacteria with resistance to specific antibiotics is one of the greatest threats to the global health system. According to the World Health Organization, antimicrobial resistance has already reached alarming levels in many parts of the world, involving a social and economic burden for the patient, for the system, and for society in general. Because of the critical health status of patients in the intensive care unit (ICU), time is critical to identify bacteria and their resistance to antibiotics. Since common antibiotics resistance tests require between 24 and 48 h after the culture is collected, we propose to apply machine learning (ML) techniques to determine whether a bacterium will be resistant to different families of antimicrobials. For this purpose, clinical and demographic features from the patient, as well as data from cultures and antibiograms are considered. From a population point of view, we also show graphically the relationship between different bacteria and families of antimicrobials by performing correspondence analysis. Results of the ML techniques evidence non-linear relationships helping to identify antimicrobial resistance at the ICU, with performance dependent on the family of antimicrobials. A change in the trend of antimicrobial resistance is also evidenced.