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ObjectivesTo determine the prevalence and factors associated with persistent pulmonary hypertension (PH) following transcatheter aortic valve replacement (TAVR) and its relationship with long-term mortality.MethodsConsecutive patients who underwent TAVR from July 2011 through January 2016 were studied. The prevalence of baseline PH (mean pulmonary artery pressure ≥25 mm Hg on right heart catheterisation) and the prevalence and the predictors of persistent≥moderate PH (pulmonary artery systolic pressure (PASP)>45 mm Hg on 1 month post-TAVR transthoracic Doppler echocardiography) were collected. Cox models quantified the effect of persistent PH on subsequent mortality while adjusting for confounders.ResultsOf the 407 TAVR patients, 273 (67%) had PH at baseline. Of these, 102 (25%) had persistent≥moderate PH. Mortality at 2 years in patients with no baseline PH versus those with PH improvement (follow-up PASP≤45 mm Hg) versus those with persistent≥moderate PH was 15.4%, 16.6% and 31.3%, respectively (p=0.049). After adjusting for Society of Thoracic Surgeons Predicted Risk of Mortality and baseline right ventricular function (using tricuspid annular plane systolic excursion), persistent≥moderate PH remained associated with all-cause mortality (HR=1.82, 95% CI 1.06 to 3.12, p=0.03). Baseline characteristics associated with increased likelihood of persistent≥moderate PH were ≥moderate tricuspid regurgitation, ≥moderate mitral regurgitation, atrial fibrillation/flutter, early (E) to late (A) ventricular filling velocities (E/A ratio) and left atrial volume index.ConclusionsPersistency of even moderate or greater PH at 1 month post-TAVR is common and associated with higher all-cause mortality.

Processes for transferring patients to higher acuity facilities lack a standardized approach to prognostication, increasing the risk for low value care that imposes significant burdens on patients and their families with unclear benefits. We sought to develop a rapid and feasible tool for predicting mortality using variables readily available at the time of hospital transfer. All work was carried out at a single, large, multi-hospital integrated healthcare system. We used a retrospective cohort for model development consisting of patients aged 18 years or older transferred into the healthcare system from another hospital, hospice, skilled nursing or other healthcare facility with an admission priority of direct emergency admit. The cohort was randomly divided into training and test sets to develop first a 54-variable, and then a 14-variable gradient boosting model to predict the primary outcome of all cause in-hospital mortality. Secondary outcomes included 30-day and 90-day mortality and transition to comfort measures only or hospice care. For model validation, we used a prospective cohort consisting of all patients transferred to a single, tertiary care hospital from one of the 3 referring hospitals, excluding patients transferred for myocardial infarction or maternal labor and delivery. Prospective validation was performed by using a web-based tool to calculate the risk of mortality at the time of transfer. Observed outcomes were compared to predicted outcomes to assess model performance. The development cohort included 20,985 patients with 1,937 (9.2%) in-hospital mortalities, 2,884 (13.7%) 30-day mortalities, and 3,899 (18.6%) 90-day mortalities. The 14-variable gradient boosting model effectively predicted in-hospital, 30-day and 90-day mortality (c = 0.903 [95% CI:0.891-0.916]), c = 0.877 [95% CI:0.864-0.890]), and c = 0.869 [95% CI:0.857-0.881], respectively). The tool was proven feasible and valid for bedside implementation in a prospective cohort of 679 sequentially transferred patients for whom the bedside nurse calculated a SafeNET score at the time of transfer, taking only 4-5 minutes per patient with discrimination consistent with the development sample for in-hospital, 30-day and 90-day mortality (c = 0.836 [95%CI: 0.751-0.921], 0.815 [95% CI: 0.730-0.900], and 0.794 [95% CI: 0.725-0.864], respectively). The SafeNET algorithm is feasible and valid for real-time, bedside mortality risk prediction at the time of hospital transfer. Work is ongoing to build pathways triggered by this score that direct needed resources to the patients at greatest risk of poor outcomes.