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Nonbacterial thrombotic endocarditis is characterized by the presence of organized thrombi on cardiac valves, often associated with hypercoagulable states. There is a paucity of data regarding the predictors of mortality in patients with nonbacterial thrombotic endocarditis. Our primary aim was to identify predictors of in-hospital mortality in patients with nonbacterial thrombotic endocarditis.A systematic literature review of all published cases and case series was performed until May 2018 according to Preferred Reporting Items for Systematic Review and Meta-analyses statement guidelines. We applied random forest machine learning model to identify predictors of in-patient mortality in patients with nonbacterial thrombotic endocarditis.Our search generated a total of 163 patients (mean age, 46 ± 17 years; women, 69%) with newly diagnosed nonbacterial thrombotic endocarditis. The in-hospital mortality rate in the study cohort was 30%. Among the patients who died in the hospital, initial presentation of pulmonary embolism (12.2 vs. 2.6%), splenic (38.7 vs. 10.5%), and renal (40.8 vs. 9.6%) infarcts were higher compared to patients alive at the time of discharge. Higher rates of malignancy (71.4 vs. 39.4%, P = .0003) and lower rates of antiphospholipid syndrome (8.1 vs. 48.2%, P = .0001) were noted in deceased patients. Random forest machine learning analysis showed that older age, presence of antiphospholipid syndrome, splenic infarct, renal infarct, peripheral thromboembolism, pulmonary embolism, myocardial infarction, and mitral valve regurgitation were significantly associated with increased risk of in-hospital mortality.Patients admitted with nonbacterial thrombotic endocarditis have a high rate of in-hospital mortality. Factors including older age, presence of antiphospholipid syndrome, splenic/renal infarct, lower limb thromboembolism, pulmonary embolism, myocardial infarction, and mitral valve regurgitation were significantly associated with increased risk of in-hospital mortality in patients with nonbacterial thrombotic endocarditis.

Background/purpose Machine learning has been used to predict procedural risk in patients undergoing various medical interventions and procedures. One-year mortality in patients after Transcatheter Aortic Valve Replacement (TAVR) has a wide range (from 8.5 to 24% in various studies). We sought to apply machine learning to determine predictors of one year mortality in patients undergoing TAVR. Methods/materials A retrospective study of 1055 patients who underwent TAVR (Jan 2014–June 2017) with one-year follow up was completed. Baseline demographics, clinical, electrocardiography (ECG), Computed Tomography (CT) and echocardiography data were abstracted. Variables with near zero variance or ≥50% missing data were excluded. The Gradient Boosting Machine learning (GBM) prediction model included 163 variables and was optimized using 5-fold cross-validation repeated 10-times. The receiver operator characteristic (ROC) for the GBM model was calculated to predict one-year mortality post TAVR, and then compared to the TAVI2-SCORE and CoreValve score. Results Among 1055 TAVR patients (mean age 80.9 ± 7.9 years, 42% female), 14.02% died at one year. 78% had balloon expandable valves placed. Based on GBM, the ten most predictive variables for one-year survival were cardiac power index, hemoglobin, systolic blood pressure, INR, diastolic blood pressure, body mass index, valve calcium score, serum creatinine, aortic annulus area, and albumin. The area under ROC to predict survival for the GBM model vs TAVI2-SCORE and CoreValve Score was 0.72 (95% CI 0.68–0.78) vs 0.56 (95%CI 0.51–0.62) and 0.53 (95% CI 0.47–0.59) respectively with p Conclusion The GBM model outperforms TAVI2-SCORE and CoreValve Score in predicting mortality one-year post TAVR.

Introduction: Atrial flutter (AFL) with figure-of-eight reentry (FOER) is effectively terminated by catheter ablation (CA) of the shared isthmus. Case: A 50-year-old man with prior mitral valve (MV) repair presented to our hospital with palpitations for 9 months refractory to metoprolol. ECG showed coarse atrial fibrillation at 61 bpm. Echocardiography showed left ventricular LVEF of 45%. Electrophysiology study revealed AFL (cycle length, TCL 290 msec) with a concentric activation pattern on the coronary sinus (CS) catheter, and a chevron pattern on the Halo catheter placed against the lateral tricuspid annulus (TA). Entrainment map revealed a near equal post-pacing interval and TCL in proximal CS, low lateral right atrium (RA), and CTI. Incomplete line of block was noted in the anterior wall of RA near the superior vena cava (SVC), likely from prior atriotomy scar. Double loop reentry (DLR) was demonstrated with one wavefront (WV) traveling counterclockwise around the TA using the CTI, and the other WV traveling posteriorly around the SVC (Figure). After CTI ablation, TCL changed from 290 msec to 330 msec suggesting blocked CTI. Now, the WV was a single loop around the SVC, confirmed by repeat activation mapping. Linear ablation from anterior TA to the anterior aspect of the SVC terminated the AFL. Differential pacing from either side of the anterior line and the CTI demonstrated a conduction time of > 200 msec and 150 msec in either direction, respectively. Decremental atrial pacing was unable to induce any arrhythmias. Discussion: FOER in this case is suggested by fusion of WVs in the lateral RA. The 2 circuits each had their own conduction gap, in the CTI and anterior RA wall near the SVC, respectively. DLR was proven by entrainment as well as activation mapping. The faster CTI-dependent AFL was entraining the upper loop reentry circuit. Conclusion: Atypical right AFL with DLR mimicking FOER was successfully terminated by CA of CTI and anterior line of ablation from TA to SVC.

Introduction: Left atrial tachycardia (AT) is uncommon and usually does not occur in the absence of congenital heart disease, or prior catheter ablation (CA). Case: A 29-year-old woman with a 2-year history of SVT refractory to metoprolol and no prior CA presented to the emergency department with palpitations. Initial ECG showed a regular, narrow complex, short RP tachycardia, at 180 bpm, insensitive to adenosine and 2 electrical cardioversions. Echocardiogram showed LVEF of 40%. The patient underwent an entirely non-fluoroscopic electrophysiology study using intracardiac echocardiography and electroanatomic mapping (EAM). AT (cycle length 300 ms) was induced by programmed atrial stimulation with isoproterenol infusion. AT was confirmed with ventriculo-atrial dissociation after pacing maneuvers and adenosine administration. Right atrial (RA) activation map revealed early atrial activation in the mid coronary sinus (CS), raising suspicion of a left atrial (LA) origin. LA was accessed via transseptal puncture. The site of earliest atrial activation was in the LA wall inferior to the left inferior pulmonary vein (LIPV) ostium and was successfully terminated with radiofrequency ablation (40 W, 5 to 10 g) (Figure) with no further inducibility. The patient's symptoms resolved after the procedure. Discussion: Left AT usually originates from PVs or the atrial wall. The EAM in this patient showed centrifugal activation consistent with a focal mechanism. Adenosine is generally not helpful in terminating AT but is useful in diagnosis. Rapid conduction across the LA to CS may have given rise to early activation in the mid CS on RA mapping. However, LA mapping revealed that atrial activation was earliest in the LA wall inferior to the LIPV. Conclusion: AT is unusual in young, healthy women. A high index of suspicion to perform transseptal puncture can lead to successful ablation of AT with LA origin. We surmise that the patient’s EF may improve with termination of the tachycardia.

Machine learning has been used to predict procedural risk in patients undergoing various medical interventions and procedures. One-year mortality in patients after Transcatheter Aortic Valve Replacement (TAVR) has a wide range (from 8.5 to 24% in various studies). We sought to apply machine learning to determine predictors of one year mortality in patients undergoing TAVR.A retrospective study of 1055 patients who underwent TAVR (Jan 2014-June 2017) with one-year follow up was completed. Baseline demographics, clinical, electrocardiography (ECG), Computed Tomography (CT) and echocardiography data were abstracted. Variables with near zero variance or ≥50% missing data were excluded. The Gradient Boosting Machine learning (GBM) prediction model included 163 variables and was optimized using 5-fold cross-validation repeated 10-times. The receiver operator characteristic (ROC) for the GBM model was calculated to predict one-year mortality post TAVR, and then compared to the TAVIAmong 1055 TAVR patients (mean age 80.9 ± 7.9 years, 42% female), 14.02% died at one year. 78% had balloon expandable valves placed. Based on GBM, the ten most predictive variables for one-year survival were cardiac power index, hemoglobin, systolic blood pressure, INR, diastolic blood pressure, body mass index, valve calcium score, serum creatinine, aortic annulus area, and albumin. The area under ROC to predict survival for the GBM model vs TAVIThe GBM model outperforms TAVI

Therapeutic dose monitoring is widely adopted for determination of busulfan (Bu) dose for use as a conditioning regimen. However, while dose adjustments are being incorporated, transient fluctuations of Bu levels may occur. We aim to understand if these fluctuations affect clinical outcomes of these patients. We compared outcomes in patients in whom the absolute dose changes and fluctuation of AUC were ≥ median% versus median%. Rates of sinusoidal obstructive syndrome, grades 2-4/grades 3-4 acute and chronic graft versus host disease were not different between the two cohorts. The Kaplan-Meier curves for overall survival showed no significant differences. Six patients required50% dose adjustment and four had a fluctuation in AUC of50%. One of these patients died of sinusoidal obstruction syndrome and two died of infections. In our study, the transient fluctuations in Bu levels did not affect clinical outcomes; hence obviating the need for test dose strategy.