High- versus low-gradient aortic stenosis: Is our evaluation limited by aorto-mitral angle on cardiovascular CT?

BACKGROUND: Accurate assessment of aortic valve (AV) stenosis (AS) on transthoracic echocardiogram is crucial for appropriate clinical management. However, discordance between aortic valve area (AVA) and Doppler can complicate the diagnosis of severe AS in low-gradient (LG) AS phenotypes. METHODS: We reviewed 220 consecutive patients with suspected severe AS and AVA ≤1.0 cm2 on transthoracic echocardiogram who were evaluated for transcatheter AV replacement (TAVR) within a large health system from 2015 to 2019. We compared AV calcium score and aorta-mitral angle (AMA) on 3-chamber views from ECG-gated cardiovascular CT among patients with high-gradient (HG) AS (N = 18), paradoxical low-flow low-gradient (PLFLG) AS (N = 24) and normal-flow low-gradient (NFLG) AS (N = 14). RESULTS: All groups had comparable age, comorbidities, and AV calcium scores. Compared to patients with HG AS (mean AMA 120 ±â€¯10°), those with PLFLG AS (104 ±â€¯12°; p < 0.001) and NFLG AS (106 ±â€¯13°; p = 0.008) had narrower mean AMA values on cardiovascular CT. CONCLUSION: LG AS patients have significantly narrower AMA than HG AS patients on cardiovascular CT. Due to difficulty obtaining parallel Doppler alignment, narrower AMA may contribute to AVA-Doppler discordance on echocardiogram. These findings emphasize the need for additional information in the setting of LG AS.

Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound.

Background: Point-of-care ultrasonography (POCUS) enables access to cardiac imaging directly at the bedside but is limited by brief acquisition, variation in acquisition quality, and lack of advanced protocols. Objective: To develop and validate deep learning models for detecting underdiagnosed cardiomyopathies on cardiac POCUS, leveraging a novel acquisition quality-adapted modeling strategy. Methods: To develop the models, we identified transthoracic echocardiograms (TTEs) of patients across five hospitals in a large U.S. health system with transthyretin amyloid cardiomyopathy (ATTR-CM, confirmed by Tc99m-pyrophosphate imaging), hypertrophic cardiomyopathy (HCM, confirmed by cardiac magnetic resonance), and controls enriched for the presence of severe AS. In a sample of 290,245 TTE videos, we used novel augmentation approaches and a customized loss function to weigh image and view quality to train a multi-label, view agnostic video-based convolutional neural network (CNN) to discriminate the presence of ATTR-CM, HCM, and/or AS. Models were tested across 3,758 real-world POCUS videos from 1,879 studies in 1,330 independent emergency department (ED) patients from 2011 through 2023. Results: Our multi-label, view-agnostic classifier demonstrated state-of-the-art performance in discriminating ATTR-CM (AUROC 0.98 [95%CI: 0.96-0.99]) and HCM (AUROC 0.95 [95% CI: 0.94-0.96]) on standard TTE studies. Automated metrics of anatomical view correctness confirmed significantly lower quality in POCUS vs TTE videos (median view classifier confidence of 0.63 [IQR: 0.44-0.88] vs 0.93 [IQR: 0.69-1.00], p<0.001). When deployed to POCUS videos, our algorithm effectively discriminated ATTR-CM and HCM with AUROC of up to 0.94 (parasternal long-axis (PLAX)), and 0.85 (apical 4 chamber), corresponding to positive diagnostic odds ratios of 46.7 and 25.5, respectively. In total, 18/35 (51.4%) of ATTR-CM and 32/57 (41.1%) of HCM patients in the POCUS cohort had an AI-positive screen in the year before their eventual confirmatory imaging. Conclusions: We define and validate an AI framework that enables scalable, opportunistic screening of under-diagnosed cardiomyopathies using POCUS.

A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression.

Importance: Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization. Objective: To deploy DASSi to patients with no AS or with mild or moderate AS at baseline to identify AS development and progression. Design, Setting, and Participants: This is a cohort study that examined 2 cohorts of patients without severe AS undergoing echocardiography in the Yale New Haven Health System (YNHHS; 2015-2021) and Cedars-Sinai Medical Center (CSMC; 2018-2019). A novel computational pipeline for the cross-modal translation of DASSi into cardiac magnetic resonance (CMR) imaging was further developed in the UK Biobank. Analyses were performed between August 2023 and February 2024. Exposure: DASSi (range, 0-1) derived from AI applied to echocardiography and CMR videos. Main Outcomes and Measures: Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR). Results: A total of 12 599 participants were included in the echocardiographic study (YNHHS: n = 8798; median [IQR] age, 71 [60-80] years; 4250 [48.3%] women; median [IQR] follow-up, 4.1 [2.4-5.4] years; and CSMC: n = 3801; median [IQR] age, 67 [54-78] years; 1685 [44.3%] women; median [IQR] follow-up, 3.4 [2.8-3.9] years). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increment: YNHHS, 0.033 m/s per year [95% CI, 0.028-0.038] among 5483 participants; CSMC, 0.082 m/s per year [95% CI, 0.053-0.111] among 1292 participants), with values of 0.2 or greater associated with a 4- to 5-fold higher AVR risk than values less than 0.2 (YNHHS: 715 events; adjusted hazard ratio [HR], 4.97 [95% CI, 2.71-5.82]; CSMC: 56 events; adjusted HR, 4.04 [95% CI, 0.92-17.70]), independent of age, sex, race, ethnicity, ejection fraction, and AV-Vmax. This was reproduced across 45 474 participants (median [IQR] age, 65 [59-71] years; 23 559 [51.8%] women; median [IQR] follow-up, 2.5 [1.6-3.9] years) undergoing CMR imaging in the UK Biobank (for participants with DASSi ≥0.2 vs those with DASSi <.02, adjusted HR, 11.38 [95% CI, 2.56-50.57]). Saliency maps and phenome-wide association studies supported associations with cardiac structure and function and traditional cardiovascular risk factors. Conclusions and Relevance: In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker was independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.

Study Protocol for the Artificial Intelligence-Driven Evaluation of Structural Heart Diseases Using Wearable Electrocardiogram (ID-SHD).

Introduction: Portable devices capable of electrocardiogram (ECG) acquisition have the potential to enhance structural heart disease (SHD) management by enabling early detection through artificial intelligence-ECG (AI-ECG) algorithms. However, the performance of these AI algorithms for identifying SHD in a real-world screening setting is unknown. To address this gap, we aim to evaluate the validity of our wearable-adapted AI algorithm, which has been previously developed and validated for detecting SHD from single-lead portable ECGs in patients undergoing routine echocardiograms in the Yale New Haven Hospital (YNHH). Research Methods and Analysis: This is the protocol for a cross-sectional study in the echocardiographic laboratories of YNHH. The study will enroll 585 patients referred for outpatient transthoracic echocardiogram (TTE) as part of their routine clinical care. Patients expressing interest in participating in the study will undergo a screening interview, followed by enrollment upon meeting eligibility criteria and providing informed consent. During their routine visit, patients will undergo a 1-lead ECG with two devices - one with an Apple Watch and the second with another portable 1-lead ECG device. With participant consent, these 1-lead ECG data will be linked to participant demographic and clinical data recorded in the YNHH electronic health records (EHR). The study will assess the performance of the AI-ECG algorithm in identifying SHD, including left ventricular systolic dysfunction (LVSD), valvular disease and severe left ventricular hypertrophy (LVH), by comparing the algorithm's results with data obtained from TTE, which is the established gold standard for diagnosing SHD. Ethics and Dissemination: All patient EHR data required for assessing eligibility and conducting the AI-ECG will be accessed through secure servers approved for protected health information. Data will be maintained on secure, encrypted servers for a minimum of five years after the publication of our findings in a peer-reviewed journal, and any unanticipated adverse events or risks will be reported by the principal investigator to the Yale Institutional Review Board, which has reviewed and approved this protocol (Protocol Number: 2000035532).

Factors associated with reporting left ventricular ejection fraction with 3D echocardiography in real-world practice.

BACKGROUND: Guidelines recommend 3D echocardiography (3DE) to assess left ventricular ejection fraction (LVEF) on transthoracic echocardiogram (TTE) when possible, but it is unclear which factors are most strongly associated with reporting 3DE LVEF in real-world practice. METHODS: We evaluated 3DE LVEF reporting by age, sex, BMI, TTE location and variation in reporting by sonographer and reader. All TTEs were performed without contrast enhancement agent at a large medical center from 9/2015 to 12/2020 using ultrasound machines capable of 3DE. We used multivariable logistic regression to assess which factors were most associated with reporting 3DE LVEF. RESULTS: Among 35 641 TTEs included in this study, 57.4% were performed on women. 3DE LVEF was reported on 18 391 TTEs (51.6% of cohort; 50.5% for women and 52.4% for men). Portable inpatient TTEs (n = 5569) had the lowest rates of 3DE LVEF reporting (30.9%), while general outpatient TTEs (n = 15 933) had greater reporting (56.9%). Outpatient TTEs with an indication for chemotherapy (n = 3244) had the highest rates of 3DE LVEF (87.2%). The median (IQR) percentage of TTEs reporting 3D LVEF was 52.7% (43.1%-68.1%) among sonographers and 51.6% (46.5%-59.6%) among readers. Among 20082 (56.3%) TTEs with 3DE LVEF measured by sonographers, 91.6% were included by readers in the final report. After adjustment, performing sonographer in the highest reporting quartile was most strongly associated with reporting 3DE LVEF (OR 7.04, 95% CI 6.55-7.56), while an inpatient portable study had the strongest negative association for reporting (OR .38, 95% CI .35-.40). CONCLUSIONS: Use of 3DE LVEF in real-world practice varies substantially based on performing sonographer and is low for hospitalized patients, but can be frequently used for chemotherapy. Initiatives are needed to increase sonographer 3DE acquisition in most clinical settings.

A Multimodality Video-Based AI Biomarker For Aortic Stenosis Development And Progression.

Importance: Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. Objective: A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler. Here, we deploy DASSi to patients with no or mild/moderate AS at baseline to identify AS development and progression. Design Setting and Participants: We defined two cohorts of patients without severe AS undergoing echocardiography in the Yale-New Haven Health System (YNHHS) (2015-2021, 4.1[IQR:2.4-5.4] follow-up years) and Cedars-Sinai Medical Center (CSMC) (2018-2019, 3.4[IQR:2.8-3.9] follow-up years). We further developed a novel computational pipeline for the cross-modality translation of DASSi into cardiac magnetic resonance (CMR) imaging in the UK Biobank (2.5[IQR:1.6-3.9] follow-up years). Analyses were performed between August 2023-February 2024. Exposure: DASSi (range: 0-1) derived from AI applied to echocardiography and CMR videos. Main Outcomes and Measures: Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR). Results: A total of 12,599 participants were included in the echocardiographic study (YNHHS: n=8,798, median age of 71 [IQR (interquartile range):60-80] years, 4250 [48.3%] women, and CSMC: n=3,801, 67 [IQR:54-78] years, 1685 [44.3%] women). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increments: YNHHS: +0.033 m/s/year [95%CI:0.028-0.038], n=5,483, and CSMC: +0.082 m/s/year [0.053-0.111], n=1,292), with levels ≥ vs <0.2 linked to a 4-to-5-fold higher AVR risk (715 events in YNHHS; adj.HR 4.97 [95%CI: 2.71-5.82], 56 events in CSMC: 4.04 [0.92-17.7]), independent of age, sex, ethnicity/race, ejection fraction and AV-Vmax. This was reproduced across 45,474 participants (median age 65 [IQR:59-71] years, 23,559 [51.8%] women) undergoing CMR in the UK Biobank (adj.HR 11.4 [95%CI:2.56-50.60] for DASSi ≥vs<0.2). Saliency maps and phenome-wide association studies supported links with traditional cardiovascular risk factors and diastolic dysfunction. Conclusions and Relevance: In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker is independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.

Severe aortic stenosis detection by deep learning applied to echocardiography.

BACKGROUND AND AIMS: Early diagnosis of aortic stenosis (AS) is critical to prevent morbidity and mortality but requires skilled examination with Doppler imaging. This study reports the development and validation of a novel deep learning model that relies on two-dimensional (2D) parasternal long axis videos from transthoracic echocardiography without Doppler imaging to identify severe AS, suitable for point-of-care ultrasonography. METHODS AND RESULTS: In a training set of 5257 studies (17 570 videos) from 2016 to 2020 [Yale-New Haven Hospital (YNHH), Connecticut], an ensemble of three-dimensional convolutional neural networks was developed to detect severe AS, leveraging self-supervised contrastive pretraining for label-efficient model development. This deep learning model was validated in a temporally distinct set of 2040 consecutive studies from 2021 from YNHH as well as two geographically distinct cohorts of 4226 and 3072 studies, from California and other hospitals in New England, respectively. The deep learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.978 (95% CI: 0.966, 0.988) for detecting severe AS in the temporally distinct test set, maintaining its diagnostic performance in geographically distinct cohorts [0.952 AUROC (95% CI: 0.941, 0.963) in California and 0.942 AUROC (95% CI: 0.909, 0.966) in New England]. The model was interpretable with saliency maps identifying the aortic valve, mitral annulus, and left atrium as the predictive regions. Among non-severe AS cases, predicted probabilities were associated with worse quantitative metrics of AS suggesting an association with various stages of AS severity. CONCLUSION: This study developed and externally validated an automated approach for severe AS detection using single-view 2D echocardiography, with potential utility for point-of-care screening.

Two- Versus 8-Zone Lung Ultrasound in Heart Failure: Analysis of a Large Data Set Using a Deep Learning Algorithm.

OBJECTIVE: Scanning protocols for lung ultrasound often include 8 or more lung zones, which may limit real-world clinical use. We sought to compare a 2-zone, anterior-superior thoracic ultrasound protocol for B-line artifact detection with an 8-zone approach in patients with known or suspected heart failure using a deep learning (DL) algorithm. METHODS: Adult patients with suspected heart failure and B-lines on initial lung ultrasound were enrolled in a prospective observational study. Subjects received daily ultrasounds with a hand-held ultrasound system using an 8-zone protocol (right and left anterior/lateral and superior/inferior). A previously published deep learning algorithm that rates severity of B-lines on a 0-4 scale was adapted for use on hand-held ultrasound full video loops. Average severities for 8 and 2 zones were calculated utilizing DL ratings. Bland-Altman plot analyses were used to assess agreement and identify bias between 2- and 8-zone scores for both primary (all patients, 5728 videos, 205 subjects) and subgroup (confirmed diagnosis of heart failure or pulmonary edema, 4464 videos, 147 subjects) analyses. RESULTS: Bland-Altman plot analyses revealed excellent agreement for both primary and subgroup analyses. The absolute difference on the 4-point scale between 8- and 2-zone average scores was not significant for the primary dataset (0.03; 95% CI -0.01 to 0.07) or the subgroup (0.01; 95% CI -0.04 to 0.06). CONCLUSION: Utilization of a 2-zone, anterior-superior thoracic ultrasound protocol provided similar severity information to an 8-zone approach for a dataset of subjects with known or suspected heart failure.

Abnormal left atrial body stiffness is predicted by appendage size: impact of appendage occlusion on left atrial mechanics assessed by pressure-volume analysis.

Atrial cardiomyopathy has been recognized as having important consequences for cardiac performance and clinical outcomes. The pathophysiological role of the left atrial (LA) appendage and the effect of percutaneous left atrial appendage occlusion (LAAO) upon LA mechanics is incompletely understood. We evaluated if changes in LA stiffness due to endocardial LAAO can be detected by LA pressure-volume (PV) analysis and whether stiffness parameters are associated with baseline characteristics. Patients undergoing percutaneous endocardial LAAO (n = 25) were studied using a novel PV analysis using near-simultaneous three-dimensional LA volume measurements by transesophageal echocardiography (TEE) and direct invasive LA pressure measurements. LA stiffness (dP/dV, change in pressure with change in volume) was calculated before and after LAAO. Overall LA stiffness significantly increased after LAAO compared with baseline (median, 0.41-0.64 mmHg/mL; P ≪ 0.001). LA body stiffness after LAAO correlated with baseline LA appendage size by indexed maximum depth (Spearman's rank correlation coefficient Rs = 0.61; P < 0.01). LA stiffness change showed an even stronger correlation with baseline LA appendage size by indexed maximum depth (Rs = 0.70; P < 0.001). We found that overall LA stiffness increases after endocardial LAAO. Baseline LA appendage size correlates with the magnitude of increase and LA body stiffness. These findings document alteration of LA mechanics after endocardial LAAO and suggest that the LA appendage modulates overall LA compliance.NEW & NOTEWORTHY Our study documents a correlation of LA appendage remodeling with the degree of chronically abnormal LA body stiffness. In addition, we found that LA appendage size was the baseline parameter that best correlated with the magnitude of a further increase in overall LA stiffness after appendage occlusion. These findings offer insights about the LA appendage and LA mechanics that are relevant to patients at risk for adverse atrial remodeling, especially candidates for LA appendage occlusion.

Tricuspid and mitral remodelling in atrial fibrillation: a three-dimensional echocardiographic study.

AIMS: Atrial fibrillation (AF) is associated with atrial enlargement, mitral annulus (MA) and tricuspid annulus (TA) dilation, and atrial functional regurgitation (AFR). However, less is known about the impact of AF on both atrioventricular valves in those with normal and abnormal ventricular function. We aimed to compare the remodelling of the TA and MA in patients with non-valvular AF without significant AFR. METHODS AND RESULTS: Ninety-two patients referred for transoesophageal echocardiography were included and categorized into three groups: (i) AF with normal left ventricular (LV) function (Normal LV-AF), n = 36; (ii) AF with LV systolic dysfunction (LVSD-AF), n = 29; and (iii) Controls in sinus rhythm, n = 27. Three-dimensional MA and TA geometry were analysed using automated software. In patients with AF regardless of LV function, the MA and TA areas were larger compared with controls (LVSD-AF vs. Normal LV-AF vs. Controls, end-systolic MA: 5.2 ± 1.1 vs. 4.5 ± 0.7 vs. 3.9 ± 0.7 cm2/m2; end-systolic TA: 5.6 ± 1.3 vs. 5.3 ± 1.3 vs. 4.1 ± 0.7 cm2/m2; P < 0.05 for each comparison with Controls). TA and MA areas were not statistically different between the two AF groups. The TA increase over controls was greater than that of the MA in the Normal LV-AF group (27.7% vs. 15.6%, P = 0.041). Conversely, in the LVSD-AF group, MA and TA increased similarly (35.9% vs. 32.4%, P = 0.660). CONCLUSION: Patients with AF showed dilation of both TA and MA compared with patients in sinus rhythm. In patients with normal LV function, AF was associated with greater TA dilation than MA dilation whereas in patients with LVSD the TA and MA were equally dilated.

Changes in left atrial appendage orifice following percutaneous left atrial appendage closure using three-dimensional echocardiography.

Percutaneous left atrial appendage (LAA) occlusion is increasingly performed in patients with atrial fibrillation and long-term contraindications for anticoagulation. Our aim was to evaluate the effects of LAA occlusion with the Watchman device on the geometry of the LAA orifice and assess its impact on the adjacent left upper pulmonary vein (LUPV) hemodynamics. We included 50 patients who underwent percutaneous LAA occlusion with the Watchman device and had acceptable three-dimensional transesophageal echocardiography images of LAA pre- and post-device placement. We measured offline the LAA orifice diameters in the long axis, and the minimum and maximum diameters, circumference, and area in the short axis view. Eccentricity index was calculated as maximum/minimum diameter ratio. The LUPV peak S and D velocities pre- and post-procedure were also measured. Patients were elderly (mean age 76 ± 8 years), 30 (60%) were men. There was a significant increase of all LAA orifice dimensions following LAA occlusion: diameter 1 (pre-device 18.1 ± 3.2 vs. post-device 21.5 ± 3.4 mm, p < 0.001), diameter 2 (20.6 ± 3.9 vs. 22.1 ± 3.6 mm, p < 0.001), minimum diameter (17.6 ± 3.1 vs. 21.3 ± 3.4 mm, p < 0.001), maximum diameter (21.5 ± 3.9 vs. 22.4 ± 3.6 mm, p = 0.022), circumference (63.6 ± 10.7 vs. 69.6 ± 10.5 mm, p < 0.001), and area (3.1 ± 1.1 vs. 3.9 ± 1.2 cm2, p < 0.001). Eccentricity index decreased after procedure (1.23 ± 0.16 vs. 1.06 ± 0.06, p < 0.001). LUPV peak S and D velocities did not show a significant difference (0.29 ± 0.15 vs. 0.30 ± 0.14 cm/s, p = 0.637; and 0.47 ± 0.19 vs. 0.48 ± 0.20 cm/s, p = 0.549; respectively). LAA orifice stretches significantly and it becomes more circular following LAA occlusion without causing a significant impact on the LUPV hemodynamics.

Understanding the role of left and right ventricular strain assessment in patients hospitalized with COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) can cause cardiac injury resulting in abnormal right or left ventricular function (RV/LV) with worse outcomes. We hypothesized that two-dimensional (2D) speckle-tracking assessment of LV global longitudinal strain (GLS) and RV free wall strain (FWS) by transthoracic echocardiography can assist as markers for subclinical cardiac injury predicting increased mortality. METHODS: We performed 2D strain analysis via proprietary software in 48 patients hospitalized with COVID-19. Clinical information, demographics, comorbidities, and lab values were collected via retrospective chart review. The primary outcome was in-hospital mortality based on an optimized abnormal LV GLS value via ROC analysis and RV FWS. RESULTS: The optimal LV GLS cutoff to predict death was -13.8%, with a sensitivity of 85% (95% CI 55-98%) and specificity of 54% (95% CI 36-71%). Abnormal LV GLS >-13.8% was associated with a higher risk of death [unadjusted hazard ratio 5.15 (95% CI 1.13-23.45), p = 0.034], which persisted after adjustment for clinical variables. Among patients with LV ejection fraction (LVEF) >50%, those with LV GLS > -13.8% had higher mortality compared to those with LV GLS <-13.8% (41% vs. 10%, p = 0.030). RV FWS value was higher in patients with LV GLS >-13.8% (-13.7 ±â€¯5.9 vs. -19.6 ±â€¯6.7, p = 0.003), but not associated with decreased survival. CONCLUSION: Abnormal LV strain with a cutoff of >-13.8% in patients with COVID-19 is associated with significantly higher risk of death. Despite normal LVEF, abnormal LV GLS predicted worse outcomes in patients hospitalized with COVID-19. There was no mortality difference based on RV strain.

Predicting In-Hospital Mortality in Patients Undergoing Percutaneous Coronary Intervention.

BACKGROUND: Standardization of risk is critical in benchmarking and quality improvement efforts for percutaneous coronary interventions (PCIs). In 2018, the CathPCI Registry was updated to include additional variables to better classify higher-risk patients. OBJECTIVES: This study sought to develop a model for predicting in-hospital mortality risk following PCI incorporating these additional variables. METHODS: Data from 706,263 PCIs performed between July 2018 and June 2019 at 1,608 sites were used to develop and validate a new full and pre-catheterization model to predict in-hospital mortality, and a simplified bedside risk score. The sample was randomly split into a development cohort (70%, n = 495,005) and a validation cohort (30%, n = 211,258). The authors created 1,000 bootstrapped samples of the development cohort and used stepwise selection logistic regression on each sample. The final model included variables that were selected in at least 70% of the bootstrapped samples and those identified a priori due to clinical relevance. RESULTS: In-hospital mortality following PCI varied based on clinical presentation. Procedural urgency, cardiovascular instability, and level of consciousness after cardiac arrest were most predictive of in-hospital mortality. The full model performed well, with excellent discrimination (C-index: 0.943) in the validation cohort and good calibration across different clinical and procedural risk cohorts. The median hospital risk-standardized mortality rate was 1.9% and ranged from 1.1% to 3.3% (interquartile range: 1.7% to 2.1%). CONCLUSIONS: The risk of mortality following PCI can be predicted in contemporary practice by incorporating variables that reflect clinical acuity. This model, which includes data previously not captured, is a valid instrument for risk stratification and for quality improvement efforts.

Patient Awareness and Clinical Inertia: Obstacles to Hypertension Control in Rural Communities in the Dominican Republic.

BACKGROUND: Many obstacles exist for adequate hypertension control, including low individual awareness and clinical inertia (CI). In this study, we aimed to determine hypertension prevalence, awareness, treatment, and control among community residents of rural areas of Peravia in Dominican Republic (DR), followed by an assessment of CI in their primary care clinics (PCCs). METHODS: We interviewed 827 adults from 8 rural communities of Peravia. Demographics, medical history, health care information, and blood pressure (BP) were obtained. We reviewed the community PCC visits of patients with known hypertension or a BP ≥140/90, abstracting medical history and the physician's action toward uncontrolled BP. RESULTS: Of those interviewed, 57% (95% CI: 53%-60%) had hypertension, with 63% (95% CI: 59%-68%) of those aware of their diagnosis. Among individuals with hypertension, 60% (95% CI: 56%-65%) were receiving pharmacological treatment, and only 35% (95% CI: 31%-40%) were controlled. Characteristics associated with awareness were female sex, age >55 years, diabetes, private insurance, and having at least 1 health care visit within the past year. Of the 507 PCC patients reviewed, 340 (67%) had uncontrolled BP. Of these, 220 had no clinical action to address the uncontrolled BP, corresponding to a CI rate of 65%. CONCLUSIONS: Among rural communities in the DR, undiagnosed hypertension remains common, especially in individuals who are younger, uninsured, or with limited access to health care. For those seen in PCCs, therapeutic intensification to achieve controlled BP is infrequently done. Strategies to address population awareness and CI are needed to improve hypertension control.