The associations between direct and delayed critical care unit admission with mortality and readmissions among patients with heart failure

Impact of the MEESSI-AHF tool to guide disposition decision-making in patients with acute heart failure in the emergency department: a before-and-after study

OBJECTIVES

To determine the impact of risk stratification using the MEESSI-AHF (Multiple Estimation of risk based on the Emergency department Spanish Score In patients with acute heart failure) scale to guide disposition decision-making on the outcomes of ED patients with acute heart failure (AHF), and assess the adherence of emergency physicians to risk stratification recommendations.

METHODS

This was a prospective quasi-experimental study (before/after design) conducted in eight Spanish EDs which consecutively enrolled adult patients with AHF. In the pre-implementation stage, the admit/discharge decision was performed entirely based on emergency physician judgement. During the post-implementation phase, emergency physicians were advised to 'discharge' patients classified by the MEESSI-AHF scale as low risk and 'admit' patients classified as increased risk. Nonetheless, the final decision was left to treating emergency physicians. The primary outcome was 30-day all-cause mortality. Secondary outcomes were days alive and out of hospital, in-hospital mortality and 30-day post-discharge combined adverse event (ED revisit, hospitalisation or death).

RESULTS

The pre-implementation and post-implementation cohorts included 1589 and 1575 patients, respectively (median age 85 years, 56% females) with similar characteristics, and 30-day all-cause mortality was 9.4% and 9.7%, respectively (post-implementation HR=1.03, 95% CI=0.82 to 1.29). There were no differences in secondary outcomes or in the percentage of patients entirely managed in the ED without hospitalisation (direct discharge from the ED, 23.5% vs 24.4%, OR=1.05, 95% CI=0.89 to 1.24). Adjusted models did not change these results. Emergency physicians followed the MEESSI-AHF-based recommendation on patient disposition in 70.9% of cases (recommendation over-ruling: 29.1%). Physicians were more likely to over-rule the recommendation when 'discharge' was recommended (56.4%; main reason: need for hospitalisation for a second diagnosis) than when 'admit' was recommended (12.8%; main reason: no appreciation of severity of AHF decompensation by emergency physician), with an OR for over-ruling the 'discharge' compared with the 'admit' recommendation of 8.78 (95% CI=6.84 to 11.3).

CONCLUSIONS

Implementing the MEESSI-AHF risk stratification tool in the ED to guide disposition decision-making did not improve patient outcomes.

Association of neighbourhood-level material deprivation with adverse outcomes and processes of care among patients with heart failure in a single-payer healthcare system: A population-based cohort study

AIM

We studied the association between neighbourhood material deprivation, a metric estimating inability to attain basic material needs, with outcomes and processes of care among incident heart failure patients in a universal healthcare system.

METHODS AND RESULTS

In a population-based retrospective study (2007-2019), we examined the association of material deprivation with 1-year all-cause mortality, cause-specific hospitalization, and 90-day processes of care. Using cause-specific hazards regression, we quantified the relative rate of events after multiple covariate adjustment, stratifying by age ≤65 or ≥66 years. Among 395 763 patients (median age 76 [interquartile range 66-84] years, 47% women), there was significant interaction between age and deprivation quintile for mortality/hospitalization outcomes (p ≤ 0.001). Younger residents (age ≤65 years) of the most versus least deprived neighbourhoods had higher hazards of all-cause death (hazard ratio [HR] 1.19, 95% confidence interval [CI] 1.10-1.29]) and cardiovascular hospitalization (HR 1.29 [95% CI 1.19-1.39]). Older individuals (≥66 years) in the most deprived neighbourhoods had significantly higher hazard of death (HR 1.11 [95% CI 1.08-1.14]) and cardiovascular hospitalization (HR 1.13 [95% CI 1.09-1.18]) compared to the least deprived. The magnitude of the association between deprivation and outcomes was amplified in the younger compared to the older age group. More deprived individuals in both age groups had a lower hazard of cardiology visits and advanced cardiac imaging (all p < 0.001), while the most deprived of younger ages were less likely to undergo implantable cardioverter-defibrillator/cardiac resynchronization therapy-pacemaker implantation (p = 0.023), compared to the least deprived.

CONCLUSION

Patients with newly-diagnosed heart failure residing in the most deprived neighbourhoods had worse outcomes and reduced access to care than those less deprived.

Global Comparison of Readmission Rates for Patients With Heart Failure

BACKGROUND

Heart failure (HF) readmission rates are low in some jurisdictions. However, international comparisons are lacking and could serve as a foundation for identifying regional patient management strategies that could be shared to improve outcomes.

OBJECTIVES

This study sought to summarize 30-day and 1-year all-cause readmission and mortality rates of hospitalized HF patients across countries and to explore potential differences in rates globally.

METHODS

We performed a systematic review and meta-analysis using MEDLINE, Embase, and CENTRAL for observational reports on hospitalized adult HF patients at risk for readmission or mortality published between January 2010 and March 2021. We conducted a meta-analysis of proportions using a random-effects model, and sources of heterogeneity were evaluated with meta-regression.

RESULTS

In total, 24 papers reporting on 30-day and 23 papers on 1-year readmission were included. Of the 1.5 million individuals at risk, 13.2% (95% CI: 10.5%-16.1%) were readmitted within 30 days and 35.7% (95% CI: 27.1%-44.9%) within 1 year. A total of 33 papers reported on 30-day and 45 papers on 1-year mortality. Of the 1.5 million individuals hospitalized for HF, 7.6% (95% CI: 6.1%-9.3%) died within 30 days and 23.3% (95% CI: 20.8%-25.9%) died within 1 year. Substantial variation in risk across countries was unexplained by countries' gross domestic product, proportion of gross domestic product spent on health care, and Gini coefficient.

CONCLUSIONS

Globally, hospitalized HF patients exhibit high rates of readmission and mortality, and the variability in readmission rates was not explained by health care expenditure, risk of mortality, or comorbidities.

Infections in Glucose-6-Phosphate Dehydrogenase G6PD-Deficient Patients; Predictors for Infection-Related Mortalities and Treatment Outcomes

Disturbances in the count or maturity of blood cells weaken their microbial defensive capacity and render them more susceptible to infections. Glucose-6-phosphate deficient patients are affected by a genetic disease that affects cell integrity with increased liability to infections and death. We aimed to investigate the risk factors for infection mortality in this patient population. We retrospectively examined the records of G6PD adult patients with confirmed infections and collected data related to demographics, infections (pathogens, types, and treatment regimens) in addition to mortality and length of stay outcomes. Data were statistically analyzed using R Programming language to identify contributing factors to mortality and treatment regimens association with outcomes. Records of 202 unique patients over 5 years were included, corresponding to 379 microbiologically and clinically confirmed infections. Patients > 60 years [p = 0.001, OR: 5.6], number of comorbidities 4 (2–5) [p < 0.001, OR: 1.8], patients needed blood transfusion [p = 0.003, OR: 4.3]. Respiratory tract infections [p = 0.037, OR: 2.28], HAIs [p = 0.002, OR: 3.9], polymicrobial infections [p = 0.001, OR: 10.9], and concurrent infection Gram-negative [p < 0.001, OR: 7.1] were significant contributors to 28-day mortality. The history of exposure to many antimicrobial classes contributed significantly to deaths, including β-lactam/β-lactamase [p = 0.002, OR: 2.5], macrolides [p = 0.001, OR: 3.34], and β-lactams [p = 0.012, OR: 2.0]. G6PD patients are a unique population that is more vulnerable to infections. Prompt and appropriate antimicrobial therapy is warranted to combat infections. A strict application of stewardship principles (disinfection, shortening the length of stay, and controlling comorbid conditions) may be beneficial for this population. Finally, awareness of the special needs of this patient group may improve treatment outcomes.

Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure

BACKGROUND

Although risk stratification of patients with acute decompensated heart failure (HF) is important, it is unknown whether machine learning (ML) or conventional statistical models are optimal. We developed ML algorithms to predict 7-day and 30-day mortality in patients with acute HF and compared these with an existing logistic regression model at the same timepoints.

METHODS

Patients presenting to one of 86 hospitals, who were either admitted to hospital or discharged home directly from the emergency department, were randomly selected using stratified random sampling. ML approaches, including neural networks, random forest, XGBoost, and the Lasso, were compared with a validated logistic regression model for discrimination and calibration.

RESULTS

Among 12,608 patients in our analysis, lasso regression (c-statistic 0.774; 95% CI, 0.743, 0.806) performed better than other ML models for 7-day mortality but did not outperform the baseline logistic regression model (0.794; 95% CI, 0.789, 0.800). For 30-day mortality, XGBoost performed better than other ML models (c-statistic 0.759; 95% CI; 0.740, 0.779), but was not significantly better than logistic regression (c-statistic 0.755; 95% CI, 0.750, 0.762). Logistic regression demonstrated better calibration at 7 days (calibration-in-the-large 0.017; 95% CI, -0.657, 0.692, and calibration slope 0.954; 95% CI,0.769, 1.139) and at 30 days (-0.026; 95% CI, -0.374, 0.322 and 0.964; 95% CI, 0.831, 1.098), and best Brier scores, compared to ML approaches.

CONCLUSIONS

Logistic regression was comparable to ML in discrimination, but was superior to ML algorithms in calibration overall. ML algorithms for prognosis should routinely report calibration metrics in addition to discrimination.