Prediction of 10-year Risk of Incident Heart Failure in Elderly Hypertensive Population: The ANBP2 Study

An association between heart rate variability and incident heart failure in an elderly cohort

BACKGROUND

Early identification of individuals at risk of developing heart failure (HF) may improve poor prognosis. A dominant sympathetic activity is common in HF and associated with worse outcomes; however, less is known about the autonomic balance before HF.

HYPOTHESIS

A low frequency/high frequency (L-F/H-F) ratio, index of heart rate variability, and marker of the autonomic balance predict the development of HF and may improve the performance of the HF prediction model when added to traditional cardiovascular (CV) risk factors.

METHODS

Individuals in the PIVUS (Prospective Investigation of the Vasculature in Uppsala Seniors) study (n = 1016, all aged 70 years) were included. Exclusion criteria were prevalent HF, electrocardiographic QRS duration ≥130 millisecond, major arrhythmias, or conduction blocks at baseline. The association between the L-F/H-F ratio and incident HF was assessed using Cox proportional hazard analysis. The C-statistic evaluated whether adding the L-F/H-F-ratio to traditional CV risk factors improved the discrimination of incident HF.

RESULTS

HF developed in 107/836 study participants during 15 years of follow-up. A nonlinear, inverse association between the L-F/H-F ratio and incident HF was mainly driven by an L-F/H-F ratio of <30. The association curve was flat for higher values (hazard ratio, HR for the total curve = 0.78 [95% confidence interval, CI: 0.69-0.88, p < .001]; HR = 2 for L-F/H-F ratio = 10). The traditional prediction model improved by 3.3% (p < .03) when the L-F/H-F ratio was added.

CONCLUSIONS

An L-F/H-F ratio of <30 was related to incident HF and improved HF prediction when added to traditional CV risk factors.

New strategies and therapies for the prevention of heart failure in high-risk patients

Despite declines in total cardiovascular mortality rates in the United States, heart failure (HF) mortality rates as well as hospitalizations and readmissions have increased in the past decade. Increases have been relatively higher among young and middle-aged adults (<65 years). Therefore, identification of individuals HF at-risk (Stage A) or with pre-HF (Stage B) before the onset of overt clinical signs and symptoms (Stage C) is urgently needed. Multivariate risk models (e.g., Pooled Cohort Equations to Prevent Heart Failure [PCP-HF]) have been externally validated in diverse populations and endorsed by the 2022 HF Guidelines to apply a risk-based framework for the prevention of HF. However, traditional risk factors included in the PCP-HF model only account for half of an individual's lifetime risk of HF; novel risk factors (e.g., adverse pregnancy outcomes, impaired lung health, COVID-19) are emerging as important risk-enhancing factors that need to be accounted for in personalized approaches to prevention. In addition to determining the role of novel risk-enhancing factors, integration of social determinants of health (SDoH) in identifying and addressing HF risk is needed to transform the current clinical paradigm for the prevention of HF. Comprehensive strategies to prevent the progression of HF must incorporate pharmacotherapies (e.g., sodium glucose co-transporter-2 inhibitors that have also been termed the "statins" of HF prevention), intensive blood pressure lowering, and heart-healthy behaviors. Future directions include investigation of novel prediction models leveraging machine learning, integration of risk-enhancing factors and SDoH, and equitable approaches to interventions for risk-based prevention of HF.

Prediction models for cardiovascular disease risk in the hypertensive population: a systematic review

OBJECTIVE

The aim of this study was to identify, describe, and evaluate the available cardiovascular disease risk prediction models developed or validated in the hypertensive population.

METHODS

MEDLINE and the Web of Science were searched from database inception to March 2019, and all reference lists of included articles were reviewed.

RESULTS

A total of 4766 references were screened, of which 18 articles were included in the review, presenting 17 prediction models specifically developed for hypertensive populations and 25 external validations. Among the 17 prediction models, most were constructed based on randomized trials in Europe or North America to predict the risk of fatal or nonfatal cardiovascular events. The most common predictors were classic cardiovascular risk factors such as age, diabetes, sex, smoking, and SBP. Of the 17 models, only one model was externally validated. Among the 25 external validations, C-statistics ranged from 0.58 to 0.83, 0.56 to 0.75, and 0.64 to 0.78 for models developed in the hypertensive population, the general population and other specific populations, respectively. Most of the development studies and validation studies had an overall high risk of bias according to PROBAST.

CONCLUSION

There are a certain number of cardiovascular risk prediction models in patients with hypertension. The risk of bias assessment showed several shortcomings in the methodological quality and reporting in both the development and validation studies. Most models developed in the hypertensive population have not been externally validated. Compared with models developed for the general population and other specific populations, models developed for the hypertensive population do not display a better performance when validated among patients with hypertension. Research is needed to validate and improve the existing cardiovascular disease risk prediction models in hypertensive populations rather than developing completely new models.

High-Density Lipoprotein Cholesterol, Blood Urea Nitrogen, and Serum Creatinine Can Predict Severe Acute Pancreatitis

BACKGROUND AND AIMS

Early prediction of disease severity of acute pancreatitis (AP) would be helpful for triaging patients to the appropriate level of care and intervention. The aim of the study was to develop a model able to predict Severe Acute Pancreatitis (SAP).

METHODS

A total of 647 patients with AP were enrolled. The demographic data, hematocrit, High-Density Lipoprotein Cholesterol (HDL-C) determinant at time of admission, Blood Urea Nitrogen (BUN), and serum creatinine (Scr) determinant at time of admission and 24 hrs after hospitalization were collected and analyzed statistically.

RESULTS

Multivariate logistic regression indicated that HDL-C at admission and BUN and Scr at 24 hours (hrs) were independently associated with SAP. A logistic regression function (LR model) was developed to predict SAP as follows: -2.25-0.06 HDL-C (mg/dl) at admission + 0.06 BUN (mg/dl) at 24 hours + 0.66 Scr (mg/dl) at 24 hours. The optimism-corrected c-index for LR model was 0.832 after bootstrap validation. The area under the receiver operating characteristic curve for LR model for the prediction of SAP was 0.84.

CONCLUSIONS

The LR model consists of HDL-C at admission and BUN and Scr at 24 hours, representing an additional tool to stratify patients at risk of SAP.