Assessment of aortic regurgitation by transesophageal color Doppler imaging of the vena contracta: validation against an intraoperative aortic flow probe

AbstractOBJECTIVESThis study was performed to validate the accuracy of color flow vena contracta (VC) measurements of aortic regurgitation (AR) severity by comparing them to simultaneous intraoperative flow probe measurements of regurgitant fraction (RgF) and regurgitant volume (RgV).BACKGROUNDColor Doppler imaging of the vena contracta has emerged as a simple and reliable measure of the severity of valvular regurgitation. This study evaluated the accuracy of VC imaging of AR by transesophageal echocardiography (TEE).METHODSA transit-time flow probe was placed on the ascending aorta during cardiac surgery in 24 patients with AR. The flow probe was used to measure RgF and RgV simultaneously during VC imaging by TEE. Flow probe and VC imaging were interpreted separately and in blinded fashion.RESULTSA good correlation was found between VC width and RgF (r = 0.85) and RgV (r = 0.79). All six patients with VC width >6 mm had a RgF >0.50. All 18 patients with VC width <5 mm had a RgF <0.50. Vena contracta area also correlated well with both RgF (r = 0.81) and RgV (r = 0.84). All six patients with VC area >7.5 mm2had a RgF >0.50, and all 18 patients with a VC area <7.5 mm2had a RgF <0.50. In a subset of nine patients who underwent afterload manipulation to increase diastolic blood pressure, RgV increased significantly (34 ± 26 ml to 41 ± 27 ml, p = 0.042) while VC width remained unchanged (5.4 ± 2.8 mm to 5.4 ± 2.8 mm, p = 0.41).CONCLUSIONSVena contracta imaging by TEE color flow mapping is an accurate marker of AR severity. Vena contracta width and VC area correlate well with RgF and RgV obtained by intraoperative flow probe. Vena contracta width appears to be less afterload-dependent than RgV

An Integrated Process for Co-Developing and Implementing Written and Computable Clinical Practice Guidelines

The goal of this article is to describe an integrated parallel process for the co-development of written and computable clinical practice guidelines (CPGs) to accelerate adoption and increase the impact of guideline recommendations in clinical practice. From February 2018 through December 2021, interdisciplinary work groups were formed after an initial Kaizen event and using expert consensus and available literature, produced a 12-phase integrated process (IP). The IP includes activities, resources, and iterative feedback loops for developing, implementing, disseminating, communicating, and evaluating CPGs. The IP incorporates guideline standards and informatics practices and clarifies how informaticians, implementers, health communicators, evaluators, and clinicians can help guideline developers throughout the development and implementation cycle to effectively co-develop written and computable guidelines. More efficient processes are essential to create actionable CPGs, disseminate and communicate recommendations to clinical end users, and evaluate CPG performance. Pilot testing is underway to determine how this IP expedites the implementation of CPGs into clinical practice and improves guideline uptake and health outcomes

Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score

ObjectiveTo develop and validate a novel, machine learning-derived model to predict the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM).Research design and methodsUsing data from 8,756 patients free at baseline of HF, with &lt;10% missing data, and enrolled in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, we used random survival forest (RSF) methods, a nonparametric decision tree machine learning approach, to identify predictors of incident HF. The RSF model was externally validated in a cohort of individuals with T2DM using the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT).ResultsOver a median follow-up of 4.9 years, 319 patients (3.6%) developed incident HF. The RSF models demonstrated better discrimination than the best performing Cox-based method (C-index 0.77 [95% CI 0.75-0.80] vs. 0.73 [0.70-0.76] respectively) and had acceptable calibration (Hosmer-Lemeshow statistic χ2 = 9.63, P = 0.29) in the internal validation data set. From the identified predictors, an integer-based risk score for 5-year HF incidence was created: the WATCH-DM (Weight [BMI], Age, hyperTension, Creatinine, HDL-C, Diabetes control [fasting plasma glucose], QRS Duration, MI, and CABG) risk score. Each 1-unit increment in the risk score was associated with a 24% higher relative risk of HF within 5 years. The cumulative 5-year incidence of HF increased in a graded fashion from 1.1% in quintile 1 (WATCH-DM score ≤7) to 17.4% in quintile 5 (WATCH-DM score ≥14). In the external validation cohort, the RSF-based risk prediction model and the WATCH-DM risk score performed well with good discrimination (C-index = 0.74 and 0.70, respectively), acceptable calibration (P ≥0.20 for both), and broad risk stratification (5-year HF risk range from 2.5 to 18.7% across quintiles 1-5).ConclusionsWe developed and validated a novel, machine learning-derived risk score that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among outpatients with T2DM

Obesity, inflammatory and thrombotic markers, and major clinical outcomes in critically ill patients with COVID‐19 in the US

Objective This study aimed to determine whether obesity is independently associated with major adverse clinical outcomes and inflammatory and thrombotic markers in critically ill patients with COVID‐19. Methods The primary outcome was in‐hospital mortality in adults with COVID‐19 admitted to intensive care units across the US. Secondary outcomes were acute respiratory distress syndrome (ARDS), acute kidney injury requiring renal replacement therapy (AKI‐RRT), thrombotic events, and seven blood markers of inflammation and thrombosis. Unadjusted and multivariable‐adjusted models were used. Results Among the 4,908 study patients, mean (SD) age was 60.9 (14.7) years, 3,095 (62.8%) were male, and 2,552 (52.0%) had obesity. In multivariable models, BMI was not associated with mortality. Higher BMI beginning at 25 kg/m2 was associated with a greater risk of ARDS and AKI‐RRT but not thrombosis. There was no clinically significant association between BMI and inflammatory or thrombotic markers. Conclusions In critically ill patients with COVID‐19, higher BMI was not associated with death or thrombotic events but was associated with a greater risk of ARDS and AKI‐RRT. The lack of an association between BMI and circulating biomarkers calls into question the paradigm that obesity contributes to poor outcomes in critically ill patients with COVID‐19 by upregulating systemic inflammatory and prothrombotic pathways