Socio-economic status and 1 year mortality among patients hospitalized for heart failure in China

Effects of Cumulative Cognitive Function Within 1 Year of Discharge on Subsequent Mortality Among Patients Hospitalized for Acute Heart Failure: A Nationwide Prospective Cohort Study

BACKGROUND

To examine the association between cumulative cognitive function and subsequent mortality among patients hospitalized for acute heart failure (AHF).

METHODS

Based on a prospective cohort of patients hospitalized for AHF, cognitive function was measured using Mini-Cog test at admission, 1- and 12-month following discharge. Cumulative cognitive function was interpreted by cumulative Mini-Cog score and cumulative times of cognitive impairment. Outcomes included subsequent all-cause and cardiovascular mortality.

RESULTS

1 454 patients hospitalized for AHF with median follow-up of 4.76 (interquartile range [IQR]: 4.18-5.07) years were included. Tertile 1 of cumulative Mini-Cog score had the highest risk of all-cause (hazard ratio [HR]: 1.52, 95% confidence interval [CI]: 1.14-2.03) and cardiovascular mortality (HR: 1.40, 95% CI: 1.02-1.93) compared with Tertile 3; patients with ≥2 times of cognitive impairment had the highest risk of all-cause (HR: 1.34, 95% CI: 1.03-1.73) and cardiovascular mortality (HR: 1.25, 95% CI: 0.93-1.67) compared with patients without any cognitive impairment. Cumulative Mini-Cog score provided the highest incremental prognostic ability in predicting all-cause (C-statistics: 0.64, 95% CI: 0.61-0.66) and cardiovascular mortality (C-statistics: 0.63, 95% CI: 0.60-0.67) risk on the basis of Get With The Guidelines-Heart Failure score.

CONCLUSIONS

Poor cumulative cognitive function was associated with increased risk of subsequent mortality and provided incremental prognostic ability for the outcomes among patients with AHF. Longitudinal assessment and monitoring of cognitive function among patients with AHF would be of great importance in identifying patients at greater risk of self-care absence for optimizing personal disease management in clinical practice.

Educational status affects prognosis of patients with heart failure with reduced ejection fraction: A post-hoc analysis from the WARCEF trial

AIMS

The influence of social determinants of health (SDOH) on the prognosis of Heart Failure and reduced Ejection Fraction (HFrEF) is increasingly reported. We aim to evaluate the contribution of educational status on outcomes in patients with HFrEF.

METHODS

We used data from the WARCEF trial, which randomized HFrEF patients with sinus rhythm to receive Warfarin or Aspirin; educational status of patients enrolled was collected at baseline. We defined three levels of education: low, medium and high level, according to the highest qualification achieved or highest school grade attended. We analysed the impact of the educational status on the risk of the primary composite outcome of all-cause death, ischemic stroke (IS) and intracerebral haemorrhage (ICH); components of the primary outcome were also analysed as secondary outcomes.

RESULTS

2295 patients were included in this analysis; of these, 992 (43.2%) had a low educational level, 947 (41.3%) had a medium education level and the remaining 356 (15.5%) showed a high educational level. Compared to patients with high educational level, those with low educational status showed a high risk of the primary composite outcome (adjusted hazard ratio [aHR]: 1.31, 95% confidence intervals [CI] 1.02-1.69); a non-statistically significant association was observed in those with medium educational level (aHR: 1.20, 95%CI: .93-1.55). Similar results were observed for all-cause death, while no statistically significant differences were observed for IS or ICH.

CONCLUSION

Compared to patients with high educational levels, those with low educational status had worse prognosis. SDOH should be considered in patients with HFrEF.

CLINICAL TRIAL REGISTRATION

NCT00041938.

Comparison of predicting cardiovascular disease hospitalization using individual, ZIP code-derived, and machine learning model-predicted educational attainment in New York City

BACKGROUND

Area-level social determinants of health (SDOH) based on patients' ZIP codes or census tracts have been commonly used in research instead of individual SDOHs. To our knowledge, whether machine learning (ML) could be used to derive individual SDOH measures, specifically individual educational attainment, is unknown.

METHODS

This is a retrospective study using data from the Mount Sinai BioMe Biobank. We included participants that completed a validated questionnaire on educational attainment and had home addresses in New York City. ZIP code-level education was derived from the American Community Survey matched for the participant's gender and race/ethnicity. We tested several algorithms to predict individual educational attainment from routinely collected clinical and demographic data. To evaluate how using different measures of educational attainment will impact model performance, we developed three distinct models for predicting cardiovascular (CVD) hospitalization. Educational attainment was imputed into models as either survey-derived, ZIP code-derived, or ML-predicted educational attainment.

RESULTS

A total of 20,805 participants met inclusion criteria. Concordance between survey and ZIP code-derived education was 47%, while the concordance between survey and ML model-predicted education was 67%. A total of 13,715 patients from the cohort were included into our CVD hospitalization prediction models, of which 1,538 (11.2%) had a history of CVD hospitalization. The AUROC of the model predicting CVD hospitalization using survey-derived education was significantly higher than the model using ZIP code-level education (0.77 versus 0.72; p < 0.001) and the model using ML model-predicted education (0.77 versus 0.75; p < 0.001). The AUROC for the model using ML model-predicted education was also significantly higher than that using ZIP code-level education (p = 0.003).

CONCLUSION

The concordance of survey and ZIP code-level educational attainment in NYC was low. As expected, the model utilizing survey-derived education achieved the highest performance. The model incorporating our ML model-predicted education outperformed the model relying on ZIP code-derived education. Implementing ML techniques can improve the accuracy of SDOH data and consequently increase the predictive performance of outcome models.

Individual Trajectories of Health Status During the First Year of Discharge From Hospitalization for Heart Failure and Their Associations With Death in the Following Years

Background Improving health status is one of the major goals in the management of heart failure (HF). However, little is known about the long-term individual trajectories of health status in patients with acute HF after discharge. Methods and Results We enrolled 2328 patients hospitalized for HF from 51 hospitals prospectively and measured their health status via the Kansas City Cardiomyopathy Questionnaire-12 at admission and 1, 6, and 12 months after discharge, respectively. The median age of the patients included was 66 years, and 63.3% were men. Six patterns of Kansas City Cardiomyopathy Questionnaire-12 trajectories were identified by a latent class trajectory model: persistently good (34.0%), rapidly improving (35.5%), slowly improving (10.4%), moderately regressing (7.4%), severely regressing (7.5%), and persistently poor (5.3%). Advanced age, decompensated chronic HF, HF with mildly reduced ejection fraction, HF with preserved ejection fraction, depression symptoms, cognitive impairment, and each additional HF rehospitalization within 1 year of discharge were associated with unfavorable health status (moderately regressing, severely regressing, and persistently poor) (P<0.05). Compared with the pattern of persistently good, slowly improving (hazard ratio [HR], 1.50 [95% CI, 1.06-2.12]), moderately regressing (HR, 1.92 [1.43-2.58]), severely regressing (HR, 2.26 [1.54-3.31]), and persistently poor (HR, 2.34 [1.55-3.53]) were associated with increased risks of all-cause death. Conclusions One-fifth of 1-year survivors after hospitalization for HF experienced unfavorable health status trajectories and had a substantially increased risk of death during the following years. Our findings help inform the understanding of disease progression from a patient perception perspective and its relationship with long-term survival. Registration URL: https://www.clinicaltrials.gov; unique identifier: NCT02878811.

Vericiguat for the Treatment of Heart Failure with Reduced Ejection Fraction Following a Worsening Heart Failure Event: A Cost-Effectiveness Analysis from the Perspective of Chinese Healthcare Providers

BACKGROUND AND OBJECTIVE

Approximately 38 million people worldwide experience heart failure (HF), with more than 10 million in China. Heart failure exacerbations are the main cause of HF hospitalization, and hospitalizations are the main driver of HF-associated costs. Vericiguat is recommended to treat patients who have had worsening HF despite guideline-directed medical therapy. However, the cost effectiveness of adding vericiguat to the standard treatment of this population in China remains unclear. The objective of this study was to investigate the cost effectiveness of adding vericiguat to standard treatment in patients with HF in the Chinese population METHODS: A lifetime Markov model with a 1-month cycle length was developed to compare the cost effectiveness of vericiguat plus standard treatment versus standard treatment alone in Chinese patients with HF with reduced ejection fraction following an HF exacerbation, from the perspective of Chinese healthcare providers. The clinical data were obtained from the VICTORIA study. The cost was accessed from our institution or studies conducted in China. The primary outcome was the incremental cost-effectiveness ratio, representing incremental cost per incremental quality-adjusted life-year (QALY). Vericiguat was considered highly cost effective if the incremental cost-effectiveness ratio obtained was lower than 12,551 USD/QALY, cost effective if the incremental cost-effectiveness ratio was between 12,551 and 37,654.5 USD/QALY, and not cost effective if the incremental cost-effectiveness ratio was higher than 37,654.5 USD/QALY. A scenario analysis, one-way sensitivity analysis, and probabilistic sensitivity analysis were performed to test the robustness of the results.

RESULTS

For a 67-year-old patient with HF following an HF exacerbation, the lifetime cost was 17,721 USD if vericiguat plus standard treatment was given, compared to 7907 USD if standard treatment alone was prescribed. The corresponding effectiveness was 2.20 QALY and 2.10 QALY, respectively. The incremental cost-effectiveness ratio of vericiguat plus standard treatment versus standard treatment alone in Chinese patients with HF was 89,429 USD/QALY, higher than the willingness-to-pay threshold of 37654.5 USD/QALY. The scenario analysis and sensitivity analysis showed the robustness of our results.

CONCLUSIONS

The addition of vericiguat to the treatment regimen of Chinese patients with HF with reduced ejection fraction following an HF exacerbation resulted in an incremental cost-effectiveness ratio of $89,429 USD/QALY compared to standard treatment. This incremental cost-effectiveness ratio exceeds the willingness-to-pay threshold and thus, vericiguat was deemed not cost effective in the Chinese population.

Association of socioeconomic status with cardiovascular disease and cardiovascular risk factors: a systematic review and meta-analysis

Aim

Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide, and several studies have indicated the association between socioeconomic status (SES) with CVD and cardiovascular risk factors (CVRFs). It is necessary to elucidate the association of SES and CVRFs with CVD.

Subject and methods

We searched PubMed, Embase, Web of Science, and the Cochrane Library for publications, using "socioeconomic status," "cardiovascular disease," and corresponding synonyms to obtain literature. The quality of studies was evaluated using the National Institutes of Health Quality Assessment Tool (NIH-QAT). All analyses were performed using Stata V.12.0.

Results

There were 31 eligible studies included in this meta-analysis. All studies presented a low risk of bias via NIH-QAT assessment. As for CVD incidence/mortality, pooled hazard ratios (HR) of low and middle vs. high income were [HR = 1.22 (1.17-1.28); HR = 1.12 (1.09-1.16)] and [HR = 1.37 (1.21-1.56); HR = 1.19 (1.06-1.34)]. The HR of education were [HR = 1.44 (1.28-1.63); HR = 1.2 (1.11-1.3)] and [HR = 1.5 (1.22-1.83); HR = 1.13 (1.05-1.22)]. The HR of deprivation were [HR = 1.28 (1.16-1.41); HR = 1.07 (1.03-1.11)] and [HR = 1.19 (1.11-1.29); HR = 1.1 (1.02-1.17)]. SES was negatively correlated with CVD outcomes. A subgroup analysis of gender and national income level also yielded a negative correlation, and additional details were also obtained.

Conclusions

SES is inversely correlated with CVD outcomes and the prevalence of CVRFs. As for CVD incidence, women may be more sensitive to income and education. In terms of CVD mortality, men may be more sensitive to income and education, and people from low- and middle-income countries are sensitive to income and education.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10389-023-01825-4.

Change in Depressive Symptoms During the First Month of Discharge and 1-Year Clinical Outcomes in Patients Hospitalized for Heart Failure

Background The patterns of depressive symptom change during the first month after discharge, as well as their prognostic implications, and predictors of persistent or new-onset depressive symptoms are not well characterized. Methods and Results We included patients hospitalized for heart failure undergoing Patient Health Questionnaire-2 before discharge and at 1 month after discharge in a multicenter prospective cohort. We characterized 4 patterns of change in depressive symptoms-persistent, new-onset, remitted depressive symptoms, and no depressive symptom-and examined the associations between the 4 patterns and 1-year clinical outcomes. We analyzed the factors associated with persistent or new-onset depressive symptoms. A total of 4130 patients were included. Among 1175 (28.5%) symptomatic patients and 2955 (71.5%) symptom-free patients before discharge, 817 (69.5%) had remission, and 366 (12.2%) had new-onset depressive symptoms, respectively. Compared with no depressive symptom, persistent depressive symptoms were associated with an increased risk of cardiovascular death (hazard ratio [HR], 2.10 [95% CI, 1.59-2.79]) and heart failure rehospitalization (HR, 1.56 [95% CI, 1.30-1.87]); new-onset depressive symptoms were associated with an increased risk of cardiovascular death (HR, 1.78 [95%CI, 1.32-2.40]) and heart failure rehospitalization (HR, 1.54 [95% CI, 1.29-1.83]). Remitted depressive symptoms were associated with a slightly increased risk of cardiovascular death but had no significant association with heart failure rehospitalization. Patients who were female or had poor socioeconomic status, stroke history, renal dysfunction, or poor health status had a higher risk of persistent or new-onset depressive symptoms. Conclusions Sex, socioeconomic status, clinical characteristics, and health status help identify patients with high risks of depressive symptoms at 1 month after discharge. Dynamic capture of depressive symptom change during this period informs long-term risk stratifications and targets patients who require psychological interventions and social support to improve clinical outcomes. Registration URL: https://www.clinicaltrials.gov; Unique identifier (NCT02878811).