This Viewpoint reviews the pathophysiological and observational basis for speculating that angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin rece

As predicting the trajectory of COVID-19 is challenging, machine learning models could assist physicians in identifying high-risk individuals. This study compares the performance of 18 machine learning algorithms for predicting ICU admission and mortality among COVID-19 patients. Using COVID-19 patient data from the Mass General Brigham (MGB) Healthcare database, we developed and internally validated models using patients presenting to the Emergency Department (ED) between March-April 2020 (n = 3597) and further validated them using temporally distinct individuals who presented to the ED between May-August 2020 (n = 1711). We show that ensemble-based models perform better than other model types at predicting both 5-day ICU admission and 28-day mortality from COVID-19. CRP, LDH, and O₂ saturation were important for ICU admission models whereas eGFR <60 ml/min/1.73 m², and neutrophil and lymphocyte percentages were the most important variables for predicting mortality. Implementing such models could help in clinical decision-making for future infectious disease outbreaks including COVID-19.

Albuminuria—increased urine albumin excretion—is associated with cardiovascular mortality among patients with diabetes, hypertension, chronic kidney disease, or heart failure, as well as among adults with few cardiovascular risk factors. Many authors have hypothesized that albuminuria reflects widespread endothelial dysfunction, but additional work is needed to uncover whether albuminuria is directly pathologic or causative of cardiovascular disease. Urinary albumin-to-creatinine ratio is an attractive, unifying biomarker of cardiovascular, kidney, and metabolic conditions that may be useful for identifying and monitoring disease trajectory. However, albuminuria may develop through unique mechanisms across these distinct clinical phenotypes. This state-of-the-art review discusses the role of albuminuria in cardiovascular, kidney, and metabolic conditions; identifies potential pathways linking albuminuria to adverse outcomes; and provides practical approaches to screening and managing albuminuria for clinical cardiologists. Future research is needed to determine how broadly and how frequently to screen patients for albuminuria, whether it is cost-effective to treat low-grade albuminuria (10–30 mg/g), and how to equitably offer newer antiproteinuric therapies across the spectrum of cardiovascular-kidney-metabolic diseases.

Randomized controlled trials have demonstrated the efficacy of mineralocorticoid receptor (MR) antagonism in delaying chronic kidney disease (CKD) progression in diabetes; however, they have not investigated the role of aldosterone or whether these beneficial effects could be achieved in individuals without diabetes. The association between serum aldosterone concentrations and kidney disease progression was investigated among 3680 participants in the Chronic Renal Insufficiency Cohort. The primary outcome was CKD progression [defined as the composite of 50% decline in estimated glomerular filtration rate (eGFR) or end-stage kidney disease, whichever occurred first]. The associations between serum aldosterone and kidney disease outcomes were assessed using Cox proportional hazard models. At baseline, higher aldosterone concentrations were associated with a lower eGFR, lower serum potassium, greater urinary potassium, and protein excretion. Over a median follow-up of 9.6 years, 1412 participants developed CKD progression. In adjusted models, each doubling of serum aldosterone was associated with a 11% increased risk of CKD progression [hazard ratio (HR) 1.11, 95% confidence interval (CI) 1.04–1.18]. Individuals with the highest quartile of serum aldosterone had a 45% increased risk of CKD progression (HR 1.45, 95% CI 1.22–1.73) compared with the lowest quartile. The risk for CKD progression was similar regardless of whether patients had concomitant diabetes (P-interaction = 0.10). Higher serum aldosterone levels among individuals with CKD are independently associated with an increased risk for kidney disease progression, irrespective of concomitant diabetes. These findings provide mechanistic support for MR antagonists in delaying CKD progression and suggest that they may also have a role in those without diabetes. Aldosterone in chronic kidney disease and renal outcomes: study design and summary results. CI, confidence interval; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; ESKD, end-stage kidney disease; HR, hazard ratio; MR, mineralocorticoid receptor.

Coronavirus disease 2019 (COVID-19) has disproportionately affected certain vulnerable populations. Studies noted higher rates of certain comorbidities such as

Key Points: Cardiovascular–kidney–metabolic (CKM) syndrome stages 1–4 were associated with a graded risk of cardiovascular mortality in a nationally representative sample of US adults. Risk was similar between stages 0 and 1, suggesting that stage 1 represents a prime opportunity for prevention and risk mitigation. CKM staging is specific to cardiovascular mortality, given lack of a strong association with either noncardiovascular or cancer mortality. Background: It is imperative to critically evaluate the prognostic implications of cardiovascular–kidney–metabolic (CKM) syndrome staging to inform clinical practice. The primary aims of this study were to define the risk of mortality associated with each CKM syndrome stage and to determine the corresponding restricted mean survival time over a 15-year period. Methods: This was a longitudinal study of 50,678 community-dwelling US adults aged 20 years and older with baseline data for CKM stage determination participating in the 1999–2018 National Health and Nutrition Examination Survey. CKM stages were defined according to the American Heart Association presidential advisory. Fifteen-year adjusted cumulative incidences of cardiovascular mortality were calculated for each stage from confounder-adjusted survival curves using the G-formula. Results: Over a median 9.5-year follow-up, 2564 participants experienced cardiovascular death. The 15-year adjusted cumulative incidences of cardiovascular mortality were stage 0, 5.5% (95% confidence interval [CI], 1.8 to 9.3); stage 1, 5.7% (95% CI, 3.2 to 8.2); stage 2, 7.9% (95% CI, 6.8 to 9.1); stage 3, 8.7% (95% CI, 6.7 to 10.8); and stage 4, 15.2% (95% CI, 13.6 to 16.8). The absolute risk difference between CKM stage 4 and stage 0 at 15 years was 9.6% (95% CI, 5.6 to 13.6). The survival difference between CKM stage 0 and stage 4 at 15 years was 8.1 (95% CI, 8.0 to 8.2) months. Conclusions: Our findings reveal a graded risk of cardiovascular mortality associated with higher CKM syndrome stage.